BACKGROUND: Malaria epidemics are a well-described phenomenon after extreme precipitation and flooding, which account for nearly half of global disasters over the past two decades. Yet few studies have examined mitigation measures to prevent post-flood malaria epidemics. METHODS: We conducted an evaluation of a malaria chemoprevention program implemented in response to severe flooding in western Uganda. Children ≤12 years of age from one village were eligible to receive 3 monthly rounds of dihydroartemisinin-piperaquine (DP). Two neighboring villages served as controls. Malaria cases were defined as individuals with a positive rapid diagnostic test result as recorded in health center registers. We performed a difference-in-differences analysis to estimate changes in the incidence and test positivity of malaria between intervention and control villages. RESULTS: A total of 554 children received at least one round of chemoprevention with 75% participating in at least two rounds. Compared to control villages, we estimated a 53.4% reduction (aRR 0.47, 95% CI 0.34 – 0.62, p<.01) in malaria incidence and a 30% decrease in the test positivity rate (aRR=0.70, CI 0.50 - 0.97, p=0.03) in the intervention village in the six months post-intervention. The impact was greatest among children receiving the intervention, but decreased incidence was also observed in older children and adults (aRR=0.57, CI 0.38-0.84, p<.01). CONCLUSIONS: Three rounds of chemoprevention with DP delivered under pragmatic conditions reduced the incidence of malaria after severe flooding in western Uganda. These findings provide a proof-of-concept for the use of malaria chemoprevention to reduce excess disease burden associated with severe flooding.
Climate changes in the eastern part of Sahelian regions will induce an increase in rainfalls and extreme climate events. In this area, due to the intense events and floods, malaria transmission, a climate sensitive disease, is thus slowly extending in time to the drought season and in areas close to the border of the desert. Vectors can as well modify their area of breeding. Control programs must be aware of these changes to adapt their strategies.
Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model’s ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda’s diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.
In this paper, we present a nonlinear deterministic mathematical model for malaria transmission dynamics incorporating climatic variability as a factor. First, we showed the limited region and nonnegativity of the solution, which demonstrate that the model is biologically relevant and mathematically well-posed. Furthermore, the fundamental reproduction number was determined using the next-generation matrix approach, and the sensitivity of model parameters was investigated to determine the most affecting parameter. The Jacobian matrix and the Lyapunov function are used to illustrate the local and global stability of the equilibrium locations. If the fundamental reproduction number is smaller than one, a disease-free equilibrium point is both locally and globally asymptotically stable, but endemic equilibrium occurs otherwise. The model exhibits forward and backward bifurcation. Moreover, we applied the optimal control theory to describe the optimal control model that incorporates three controls, namely, using treated bed net, treatment of infected with antimalaria drugs, and indoor residual spraying strategy. The Pontryagin’s maximum principle is introduced to obtain the necessary condition for the optimal control problem. Finally, the numerical simulation of optimality system and cost-effectiveness analysis reveals that the combination of treated bed net and treatment is the most optimal and least-cost strategy to minimize the malaria.
BACKGROUND: High altitude settings in Eastern Africa have been reported to experience increased malaria burden due to vector habitat expansion. This study explored possible associations between malaria test positivity rates and its predictors including malaria control measures and meteorological factors at a high-altitude, low malaria transmission setting, south of Mount Kilimanjaro. METHODS: Malaria cases reported at the Tanganyika Plantation Company (TPC) hospital’s malaria registers, meteorological data recorded at TPC sugar factory and data on bed nets distributed in Lower Moshi from 2009 to 2018 were studied. Correlation between bed nets distributed and malaria test positivity rates were explored by using Pearson correlation analysis and the associations between malaria test positivity rates and demographic and meteorological variables were determined by logistic regression and negative binomial regression analyses, respectively. RESULTS: Malaria cases reported at TPC hospital ranged between 0.48 and 2.26% per year and increased slightly at the introduction of malaria rapid diagnostic tests. The risk of testing positive for malaria were significantly highest among individuals aged between 6 and 15 years (OR = 1.65; 1.65 CI = 1.28-2.13; p = 0.001) and 16-30 years (OR = 1.49; CI = 1.17-1.89; p = 0.001) and when adjusted for age, the risk were significantly higher among male individuals when compared to female individuals (OR = 1.54; 1.00-1.31; p = 0.044). Malaria test positivity rates were positively associated with average monthly minimum temperatures and negatively associated with average monthly maximum temperatures (incidence rate ratio (IRR) = 1.37, 95% confidence interval (CI) = 1.05-1.78, p = 0.019 and IRR = 0.72, 95% CI = 0.58-0.91, p = 0.005, respectively). When analysed with one month lag for predictor variables, malaria test positivity rates were still significantly associated with average monthly minimum and maximum temperatures (IRR = 1.67, 95% CI = 1.28-2.19, p = 0.001 and IRR = 0.68, 95% CI = 0.54-0.85, p = 0.001, respectively). Average monthly rainfall and relative humidity with or without a one month lag was not associated with malaria test positivity rates in the adjusted models. Explopring possible associations between distribution of long-lasting insecticidal nets, (LLINs) and malaria test positivity rates showed no apparent correlation between numbers of LLINs distributed in a particular year and malaria test positivity rates. CONCLUSION: In Lower Moshi, the risk of being tested positive for malaria was highest for older children and male individuals. Higher minimum and lower maximum temperatures were the strongest climatic predictors for malaria test positivity rates. In areas with extensive irrigation activity as in Lower Moshi, vector abundance and thus malaria transmission may be less dependent on rainfall patterns and humidity. Mass distribution of LLINs did not have an effect in this area with already very low malaria transmission.
BACKGROUND: Climate variables impact human health and in an era of climate change, there is a pressing need to understand these relationships to best inform how such impacts are likely to change. OBJECTIVES: This study sought to investigate time series of daily admissions from two public hospitals in Limpopo province in South Africa with climate variability and air quality. METHODS: We used wavelet transform cross-correlation analysis to monitor coincidences in changes of meteorological (temperature and rainfall) and air quality (concentrations of PM(2.5) and NO(2)) variables with admissions to hospitals for gastrointestinal illnesses including diarrhoea, pneumonia-related diagnosis, malaria and asthma cases. We were interested to disentangle meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals. RESULTS: We found preconditioning of prevalence of pneumonia by changes in air quality and showed that malaria in South Africa is a multivariate event, initiated by co-occurrence of heat and rainfall. We provided new statistical estimates of time delays between the change of weather or air pollution and increase of hospital admissions for pneumonia and malaria that are addition to already known seasonal variations. We found that increase of prevalence of pneumonia follows changes in air quality after a time period of 10 to 15 days, while the increase of incidence of malaria follows the co-occurrence of high temperature and rainfall after a 30-day interval. DISCUSSION: Our findings have relevance for early warning system development and climate change adaptation planning to protect human health and well-being.
Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.
In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population. We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables. Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.
Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m(-2)), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
BACKGROUND: Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia. METHODS: Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence. RESULTS: Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801-1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m. CONCLUSIONS: Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.
BACKGROUND: During the last two decades, researchers have suggested that the changes of malaria cases in African highlands were driven by climate change. Recently, a study claimed that the malaria cases (Plasmodium falciparum) in Oromia (Ethiopia) were related to minimum temperature. Critics highlighted that other variables could be involved in the dynamics of the malaria. The literature mentions that beyond climate change, trends in malaria cases could be involved with HIV, human population size, poverty, investments in health control programmes, among others. METHODS: Population ecologists have developed a simple framework, which helps to explore the contributions of endogenous (density-dependent) and exogenous processes on population dynamics. Both processes may operate to determine the dynamic behaviour of a particular population through time. Briefly, density-dependent (endogenous process) occurs when the per capita population growth rate (R) is determined by the previous population size. An exogenous process occurs when some variable affects another but is not affected by the changes it causes. This study explores the dynamics of malaria cases (Plasmodium falciparum and Plasmodium vivax) in Oromia region in Ethiopia and explores the interaction between minimum temperature, HIV, poverty, human population size and social instability. RESULTS: The results support that malaria dynamics showed signs of a negative endogenous process between R and malaria infectious class, and a weak evidence to support the climate change hypothesis. CONCLUSION: Poverty, HIV, population size could interact to force malaria models parameters explaining the dynamics malaria observed at Ethiopia from 1985 to 2007.
BACKGROUND: Informed decision making is underlined by all tiers in the health system. Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s malaria elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate the elimination progresses. This study was intended to assess overall reported malaria cases, reporting quality, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia. METHODS: Past 8 years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone, South Ethiopia were extracted. Malaria cases data at each health center with sociodemographic information, between January 2012 and December 2019, were included. Meteorological data were obtained from the national meteorology agency of Ethiopia. The data were analyzed using Stata 13. RESULTS: A total of 485,414 suspected cases were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases with an overall decline during the 8-year period. We noted that 3758 suspected cases and 467 confirmed malaria cases were not captured at the health offices. Based on the health centers records, the proportions of Plasmodium falciparum (49.74%) and P. vivax (47.59%) infection were nearly equivalent (p = 0.795). The former was higher at low altitudes while the latter was higher at higher altitudes. The over 15 years of age group accounted for 11.47% of confirmed malaria cases (p < 0.001). There was high spatiotemporal variation: the highest case record was during Belg (12.52%) and in Dilla town (18,150, 13.17%, p < 0.001) which is located at low altitude. Monthly rainfall and minimum temperature exhibited strong associations with confirmed malaria cases. CONCLUSION: A notable overall decline in malaria cases was observed during the eight-year period. Both P. falciparum and P. vivax were found at equivalent endemicity level; hence control measures should continue targeting both species. The noticed under reporting, the high malaria burden in urban settings, low altitudes and Belg season need spatiotemporal consideration by the elimination program.
Background: In Gabon, a new national malaria control policy was implemented in 2003. It resulted in a decrease in the number of malaria cases in the country. In March 2020, the disruption of routine health services due to the COVID-19 pandemic has led to an increase in cases and deaths due to malaria. However, in Franceville, south-east Gabon, no data on malaria cases recorded before, during and after the COVID-19 epidemic has been published. Thus, the objective of this study was to determine the epidemiological characteristics of malaria in Franceville from 2019 to 2021. Methods: A retrospectively study of malaria cases was performed at the Hopital de l’Amitie Sino-Gabonaise (HASG). Information regarding age, gender, malaria diagnosis by microscopy and hematology cell count were collected from laboratory registers from June 2019 to December 2021. Malaria data were analyzed and correlated with seasonal variations. Results: The data of 12,695 febrile patients were collected from the laboratory registers of the HASG, among which 4252 (33.5%) patients were found positive for malaria. The malaria prevalence was 37.5% in 2020 year. This prevalence was highest compared to the 2019 (29.6%) and 2021 (31.5%) year (p < 0.001). During the short rainy season (October to December), a large increase in malaria cases was observed all three year, from 2019 to 2021 (p > 0.05). Conclusion: The prevalence of malaria in Franceville was very high during COVID-19 pandemic. It is therefore necessary to strengthen existing interventions and implement more effective interventions.
Background: This study investigated malaria transmission under various contrasting settings in the Central Region, a malaria endemic region in Ghana. Methods: This cross-sectional study was carried out in five randomly selected districts in the Central Region of Ghana. Three of the districts were forested, while the rest was coastal. Study participants were selected to coincide with either the regular rainy or dry season. From each study site, hospital attendees were randomly selected with prior consent. Consciously, study participants were selected in both rainy (September and October, 2020) and dry (November and December, 2020) seasons. Clinical data for each patient was checked for clinical malaria suspicion and microscopic confirmation of malaria. Using SPSS Version 24 (Chicago, IL, USA), bivariate analysis was done to determine the association of independent variables (ecological and seasonal variations) with malaria status. When the overall analysis did not yield significant association, further statistical analysis was performed after stratification of variables (into age and gender) to determine whether any or both of them would significantly associate with the dependent variable. Results: Of the 3993 study participants, 62.5% were suspected of malaria whereas 38.2% were confirmed to have clinical falciparum malaria. Data analysis revealed that in both rainy and dry seasons, malaria cases were significantly higher in forested districts ) than coastal districts (x2 = 217.9 vs x2 = 50.9; p < 0.001). Taken together, the risk of malaria was significantly higher in the dry season (COR = 1.471, p < 0.001) and lower in coastal zones (COR = 0.826, p = 0.007). There was significant reduced risk of participants aged over 39 years of malaria (COR=0.657, p < 0.001). Whereas, in general patients between 10 and 19 years were insignificantly less likely to have malaria (COR = 0.911, p = 0.518) compared to participants aged less than < 10 years, the reverse was observed in coastal districts where patients less than 10 years of age in coastal districts were less likely to have malaria (COR=2.440, p = 0.003). In general, gender did not associate with malaria, but when stratified by study district, the risk of female gender to malaria was significantly higher in Agona Swedru (COR = 5.605, p < 0.001), Assin central (COR = 2.172, p < 0.001), Awutu Senya (COR = 2.410, p < 0.001) and Cape Coast (COR = 3.939, p < 0.001) compared to Abura-Asebu-Kwamankese. Conclusion: This study demonstrated that the predictors of malaria differ from one endemic area to another. Therefore, malaria control interventions such as distribution of long-lasting insecticide treated bed nets, residual spraying with insecticide and mass distribution of antimalaria prophylaxis must be intensified in forested districts in all seasons with particular attention on females. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. CC_BY_NC_ND_4.0
The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7-4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.
Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran’s I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff’s space-time scan statistics were used to investigate space-time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month's cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness.
Whilst climate change is expected to tremendously influence the regional transmission of malaria, the available data reveal conflicting results. This study provides contextual evidence. We adopted multi-scale geographically weighted regression (MGWR) modelling approach. AICc and local r(2) were used to evaluate performance of the MGWR.. The MGWR analysis showed that LST (beta = -0.667), maximum temperature (beta = -0.507), mean temperature (beta = -0.480), and distance from streams (beta = -0.487) were negatively associated with malaria prevalence. However, enhanced vegetation index correlated positively with malaria prevalence (beta = 0.663). Our results may be important for public health interventions.
Malaria has a significant impact on the lives of many in Ghana. It is one of the key causes of mortality and morbidity, resulting in 32.5% of outpatient visits and 48.8% of under 5-year-old hospital admissions. Future climate change may impact on this risk. This study aims at estimating the impact of climate variables and health facilities on malaria prevalence in Ghana using regional data from January 2012 to May 2017. This study links data at a regional level on malaria cases with weather data to evaluate the impact that changes in weather may have on malaria prevalence in Ghana. The results of fixed-effect modelling show that the maximum temperature has a statistically significant negative impact on malaria in the context of Ghana, and rainfall with a lag of two months has a positive statistically significant impact. Adapting to climate change in Ghana requires a better understanding of the climate-malaria relationship and this paper attempts to bridge this gap.
CONTEXT: In Mali, malaria transmission is seasonal, exposing children to high morbidity and mortality. A preventative strategy called Seasonal Malaria Chemoprevention (SMC) is being implemented, consisting of the distribution of drugs at monthly intervals for up to 4 months to children between 3 and 59 months of age during the period of the year when malaria is most prevalent. This study aimed to analyze the evolution of the incidence of malaria in the general population of the health districts of Kati, Kadiolo, Sikasso, Yorosso, and Tominian in the context of SMC implementation. METHODS: This is a transversal study analyzing the routine malaria data and meteorological data of Nasa Giovanni from 2016 to 2018. General Additive Model (GAM) analysis was performed to investigate the relationship between malaria incidence and meteorological factors. RESULTS: From 2016 to 2018, the evolution of the overall incidence in all the study districts was positively associated with the relative humidity, rainfall, and minimum temperature components. The average monthly incidence and the relative humidity varied according to the health district, and the average temperature and rainfall were similar. A decrease in incidence was observed in children under five years old in 2017 and 2018 compared to 2016. CONCLUSION: A decrease in the incidence of malaria was observed after the SMC rounds. SMC should be applied at optimal periods.
Malaria is among the greatest public health threats in Mozambique, with over 10 million cases reported annually since 2018. Although the relationship between seasonal trends in environmental parameters and malaria cases is well established, the role of climate in deviations from the annual cycle is less clear. To investigate this and the potential for leveraging inter-annual climate variability to predict malaria outbreaks, weekly district-level malaria incidence spanning 2010-2017 were processed for a cross-analysis with climate data. An empirical orthogonal function analysis of district-level malaria incidence revealed two dominant spatiotemporal modes that collectively account for 81% of the inter-annual variability of malaria: a mode dominated by variance over the southern half of Mozambique (64%), and another dominated by variance in the northern third of the country (17%). These modes of malaria variability are shown to be closely related to precipitation. Linear regression of global sea surface temperatures onto local precipitation indices over these variance maxima links the leading mode of inter-annual malarial variability to the El Nino-Southern Oscillation, such that La Nina leads to wetter conditions over southern Mozambique and, therefore, higher malaria prevalence. Similar analysis of spatiotemporal patterns of precipitation over a longer time period (1979-2019) indicate that the Subtropical Indian Ocean Dipole is both a strong predictor of regional precipitation and the climatic mechanism underlying the second mode of malarial variability. These results suggest that skillful malaria early warning systems may be developed that leverage quasi-predictable modes of inter-annual climate variability in the tropical oceans. Plain Language Summary Malaria is one of the main public health concerns in Mozambique, with millions of reported cases in the country each year. While malaria has been tied to monthly swings in rainfall and temperature, its relationship to year-to-year changes of the climate is less well known. We identified regions where local malaria cases varied together and found two main patterns: a main hotspot over the southern half of Mozambique, and a second hotspot over the northern third of the country. Rainfall drives both of these hotspots. We then tied these patterns to two natural climate phenomena, the El Nino-Southern Oscillation and the Subtropical Indian Ocean Dipole, both of which impact the climate of the region and help drive malaria prevalence. Our results suggest that it may be possible to take advantage of the predictability of these climate phenomena to improve public health planning both in Mozambique and more broadly.
Introduction: Despite the recent progress in the malaria burden, climatic factors are important if the world will achieve the set target of its eradication. Hence, this study determined the impact of climatic conditions on childhood severe malaria in a tertiary health facility in northern Nigeria. Methodology: This was a retrospective descriptive study that involved children with severe malaria managed between July 2016 and August 2017. The diagnosis of severe malaria was according to the World Health Organization 2015 guidelines. We extracted relevant data from case records and obtained the weather information from the Nigerian Meteorological Agency and www.worldweatheronline.com. Data were entered in Microsoft Excel 2013 and analyzed with Statistical Package for the Social Sciences version 20. Results: A total of 483 cases of children with severe malaria were managed. The median age was 4.0 (2.5-8.0) years. Males were 261 (54.0%). In the wet season, 375 (77.6%) cases were recorded, while 108 (22.4%) cases occurred during the dry season. The odds of malaria occurring during the wet season were 2.057 (95% CI, 1.613-2.622). Temperature patterns were not related to malaria cases. Malaria cases showed significant moderate positive cross-correlation at 2- and 3-months lag for the rainfall pattern (best cross-correlation occurred at 3 months lag with a coefficient of 0.598, p = 0.045). Conclusion: This study demonstrated marked seasonality of childhood severe malaria infection with 77% of cases during the wet season. Malaria was associated with only rainfall at a 2 to 3 months lag amongst the climatic variables. We recommend the urgent implementation of seasonal malaria chemoprophylaxis.
Several vector-borne diseases, such as malaria, are sensitive to climate and weather conditions. When unusual conditions prevail, for example, during periods of heavy rainfall, mosquito populations can multiply and trigger epidemics. This study, which consists of better understanding the link between malaria transmission and climate factors at a national level, aims to validate the VECTRI model (VECtor borne disease community model of ICTP, TRIeste) in Senegal. The VECTRI model is a grid-distributed dynamical model that couples a biological model for the vector and parasite life cycles to a simple compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) representation of the disease progression in the human host. In this study, a VECTRI model driven by reanalysis data (ERA-5) was used to simulate malaria parameters, such as the entomological inoculation rate (EIR) in Senegal. In addition to the ERA5-Land daily reanalysis rainfall, other daily rainfall data come from different meteorological products, including the CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC for Climate Prediction Center), satellite data from the African Rainfall Climatology 2.0 (ARC2), and the Climate Hazards InfraRed Precipitation with Station data (CHIRPS). Observed malaria data from the National Malaria Control Program in Senegal (PNLP/Programme National de Lutte contre le Paludisme au Senegal) and outputs from the climate data used in this study were compared. The findings highlight the unimodal shape of temporal malaria occurrence, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall, showing a south-north gradient over Senegal. This study showed that the peak of malaria takes place from September to October, with a lag of about one month from the peak of rainfall in Senegal. There is an agreement between observations and simulations about decreasing malaria cases on time. These results indicate that the southern area of Senegal is at the highest risk of malaria spread outbreaks. The findings in the paper are expected to guide community-based early-warning systems and adaptation strategies in Senegal, which will feed into the national malaria prevention, response, and care strategies adapted to the needs of local communities.
Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on aspects of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite’s reproductive cycle inside the mosquito. The rainfall intensity and frequency modulate the mosquito population’s development intensity. In this study, the Liverpool Malaria Model (LMM) was used to simulate the spatiotemporal variation of malaria incidence in Senegal. The simulations were based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and the biased-corrected CMIP6 model data, separating historical simulations and future projections for three Shared Socio-economic Pathways scenarios (SSP126, SSP245, and SSP585). Our results highlight a strong increase in temperatures, especially within eastern Senegal under the SSP245 but more notably for the SSP585 scenario. The ability of the LMM model to simulate the seasonality of malaria incidence was assessed for the historical simulations. The model revealed a period of high malaria transmission between September and November with a maximum reached in October, and malaria results for historical and future trends revealed how malaria transmission will change. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and the extreme scenario. This study is important for the planning, prioritization, and implementation of malaria control activities in Senegal.
BACKGROUND: This study aimed to assess the seasonality of confirmed malaria cases in Togo and to provide new indicators of malaria seasonality to the National Malaria Control Programme (NMCP). METHODS: Aggregated data of confirmed malaria cases were collected monthly from 2008 to 2017 by the Togo’s NMCP and stratified by health district and according to three target groups: children < 5 years old, children ≥ 5 years old and adults, and pregnant women. Time series analysis was carried out for each target group and health district. Seasonal decomposition was used to assess the seasonality of confirmed malaria cases. Maximum and minimum seasonal indices, their corresponding months, and the ratio of maximum/minimum seasonal indices reflecting the importance of malaria transmission, were provided by health district and target group. RESULTS: From 2008 to 2017, 7,951,757 malaria cases were reported in Togo. Children < 5 years old, children ≥ 5 years old and adults, and pregnant women represented 37.1%, 57.7% and 5.2% of the confirmed malaria cases, respectively. The maximum seasonal indices were observed during or shortly after a rainy season and the minimum seasonal indices during the dry season between January and April in particular. In children < 5 years old, the ratio of maximum/minimum seasonal indices was higher in the north, suggesting a higher seasonal malaria transmission, than in the south of Togo. This is also observed in the other two groups but to a lesser extent. CONCLUSIONS: This study contributes to a better understanding of malaria seasonality in Togo. The indicators of malaria seasonality could allow for more accurate forecasting in malaria interventions and supply planning throughout the year.
BACKGROUND: Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. METHODS: This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. RESULTS: Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4. CONCLUSIONS: In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.
BACKGROUND: There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda). METHODS: Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity. RESULTS: Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6-12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0-5 years old (IRR = 2.07 (1.40, 3.07)). DISCUSSION: Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models. CONCLUSION: The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health.
BACKGROUND: Seasonal patterns of malaria cases in many parts of Africa are generally associated with rainfall, yet in the dry seasons, malaria transmission declines but does not always cease. It is important to understand what conditions support these periodic cases. Aerial moisture is thought to be important for mosquito survival and ability to forage, but its role during the dry seasons has not been well studied. During the dry season aerial moisture is minimal, but intermittent periods may arise from the transpiration of peri-domestic trees or from some other sources in the environment. These periods may provide conditions to sustain pockets of mosquitoes that become active and forage, thereby transmitting malaria. In this work, humidity along with other ecological variables that may impact malaria transmission have been examined. METHODS: Negative binomial regression models were used to explore the association between peri-domestic tree humidity and local malaria incidence. This was done using sensitive temperature and humidity loggers in the rural Southern Province of Zambia over three consecutive years. Additional variables including rainfall, temperature and elevation were also explored. RESULTS: A negative binomial model with no lag was found to best fit the malaria cases for the full year in the evaluated sites of the Southern Province of Zambia. Local tree and granary night-time humidity and temperature were found to be associated with local health centre-reported incidence of malaria, while rainfall and elevation did not significantly contribute to this model. A no lag and one week lag model for the dry season alone also showed a significant effect of humidity, but not temperature, elevation, or rainfall. CONCLUSION: The study has shown that throughout the dry season, periodic conditions of sustained humidity occur that may permit foraging by resting mosquitoes, and these periods are associated with increased incidence of malaria cases. These results shed a light on conditions that impact the survival of the common malaria vector species, Anopheles arabiensis, in arid seasons and suggests how they emerge to forage when conditions permit.
The role of climate change on global malaria is often highlighted in World Health Organisation reports. We modelled a Zambian socio-environmental dataset from 2000 to 2016, against malaria trends and investigated the relationship of near-term environmental change with malaria incidence using Bayesian spatio-temporal, and negative binomial mixed regression models. We introduced the diurnal temperature range (DTR) as an alternative environmental measure to the widely used mean temperature. We found substantial sub-national near-term variations and significant associations with malaria incidence-trends. Significant spatio-temporal shifts in DTR/environmental predictors influenced malaria incidence-rates, even in areas with declining trends. We highlight the impact of seasonally sensitive DTR, especially in the first two quarters of the year and demonstrate how substantial investment in intervention programmes is negatively impacted by near-term climate change, most notably since 2010. We argue for targeted seasonally-sensitive malaria chemoprevention programmes.
BACKGROUND: Malaria epidemics are increasing in East Africa since the 1980s, coincident with rising temperature and widening climate variability. A projected 1-3.5 °C rise in average global temperatures by 2100 could exacerbate the epidemics by modifying disease transmission thresholds. Future malaria scenarios for the Lake Victoria Basin (LVB) are quantified for projected climate scenarios spanning 2006-2100. METHODS: Regression relationships are established between historical (1995-2010) clinical malaria and anaemia cases and rainfall and temperature for four East African malaria hotspots. The vector autoregressive moving average processes model, VARMAX (p,q,s), is then used to forecast malaria and anaemia responses to rainfall and temperatures projected with an ensemble of eight General Circulation Models (GCMs) for climate change scenarios defined by three Representative Concentration Pathways (RCPs 2.6, 4.5 and 8.5). RESULTS: Maximum temperatures in the long rainy (March-May) and dry (June-September) seasons will likely increase by over 2.0 °C by 2070, relative to 1971-2000, under RCPs 4.5 and 8.5. Minimum temperatures (June-September) will likely increase by over 1.5-3.0 °C under RCPs 2.6, 4.5 and 8.5. The short rains (OND) will likely increase more than the long rains (MAM) by the 2050s and 2070s under RCPs 4.5 and 8.5. Historical malaria cases are positively and linearly related to the 3-6-month running means of monthly rainfall and maximum temperature. Marked variation characterizes the patterns projected for each of the three scenarios across the eight General Circulation Models, reaffirming the importance of using an ensemble of models for projections. CONCLUSIONS: The short rains (OND), wet season (MAM) temperatures and clinical malaria cases will likely increase in the Lake Victoria Basin. Climate change adaptation and mitigation strategies, including malaria control interventions could reduce the projected epidemics and cases. Interventions should reduce emerging risks, human vulnerability and environmental suitability for malaria transmission.
BACKGROUND: Malaria has continued to be a life-threatening disease among under-five children in sub-Saharan Africa. Recent data indicate rising cases in Rwanda after some years of decline. We aimed at estimating the spatial variations in malaria prevalence at a continuous spatial scale and to quantify locations where the prevalence exceeds the thresholds of 5% and 10% across the country. We also consider the effects of some socioeconomic and climate variables. METHODS: Using data from the 2014-2015 Rwanda Demographic and Health Survey, a geostatistical modeling technique based on stochastic partial differential equation approach was used to analyze the geospatial prevalence of malaria among under-five children in Rwanda. Bayesian inference was based on integrated nested Laplace approximation. RESULTS: The results demonstrate the uneven spatial variation of malaria prevalence with some districts including Kayonza and Kirehe from Eastern province; Huye and Nyanza from Southern province; and Nyamasheke and Rusizi from Western province having higher chances of recording prevalence exceeding 5%. Malaria prevalence was found to increase with rising temperature but decreases with increasing volume for rainfall. The findings also revealed a significant association between malaria and demographic factors including place of residence, mother’s educational level, and child’s age and sex. CONCLUSIONS: Potential intervention programs that focus on individuals living in rural areas, lowest wealth quintile, and the locations with high risks should be reinforced. Variations in climatic factors particularly temperature and rainfall should be taken into account when formulating malaria intervention programs in Rwanda.
BACKGROUND: Seasonal malaria chemoprevention (SMC) involves administering antimalarial drugs at monthly intervals during the high malaria transmission period to children aged 3 to 59 months as recommended by the World Health Organization. Typically, a full SMC course is administered over four monthly cycles from July to October, coinciding with the rainy season. However, an analysis of rainfall patterns suggest that the malaria transmission season is longer and starting as early as June in the south of Burkina Faso, leading to a rise in cases prior to the first cycle. This study assessed the acceptability and feasibility of extending SMC from four to five cycles to coincide with the earlier rainy season in Mangodara health district. METHODS: The mixed-methods study was conducted between July and November 2019. Quantitative data were collected through end-of-cycle and end-of-round household surveys to determine the effect of the additional cycle on the coverage of SMC in Mangodara. The data were then compared with 22 other districts where SMC was implemented by Malaria Consortium. Eight focus group discussions were conducted with caregivers and community distributors and 11 key informant interviews with community, programme and national-level stakeholders. These aimed to determine perceptions of the acceptability and feasibility of extending SMC to five cycles. RESULTS: The extension was perceived as acceptable by caregivers, community distributors and stakeholders due to the positive impact on the health of children under five. However, many community distributors expressed concern over the feasibility, mainly due to the clash with farming activities in June. Stakeholders highlighted the need for more evidence on the impact of the additional cycle on parasite resistance prior to scale-up. End-of-cycle survey data showed no difference in coverage between five SMC cycles in Mangodara and four cycles in the 22 comparison districts. CONCLUSIONS: The additional cycle should begin early in the day in order to not coincide with the agricultural activities of community distributors. Continuous sensitisation at community level is critical for the sustainability of SMC and acceptance of an additional cycle, which should actively engage male caregivers. Providing additional support in proportion to the increased workload from a fifth cycle, including timely remuneration, is critical to avoid the demotivation of community distributors. Further studies are required to understand the effectiveness, including cost-effectiveness, of tailoring SMC according to the rainy season. Understanding the impact of an additional cycle on parasite resistance to SPAQ is critical to address key informants’ concerns around the deviation from the current four-cycle policy recommendation.
BACKGROUND: In 2012, the WHO issued a policy recommendation for the use of seasonal malaria chemoprevention (SMC) to children 3-59 months in areas of highly seasonal malaria transmission. Clinical trials have found SMC to prevent around 75% of clinical malaria. Impact under routine programmatic conditions has been assessed during research studies but there is a need to identify sustainable methods to monitor impact using routinely collected data. METHODS: Data from Demographic Health Surveys were merged with rainfall, geographical and programme data in Burkina Faso (2010, 2014, 2017) and Nigeria (2010, 2015, 2018) to assess impact of SMC. We conducted mixed-effects logistic regression to predict presence of malaria infection in children aged 6-59 months (rapid diagnostic test (RDT) and microscopy, separately). RESULTS: We found strong evidence that SMC administration decreases odds of malaria measured by RDT during SMC programmes, after controlling for seasonal factors, age, sex, net use and other variables (Burkina Faso OR 0.28, 95% CI 0.21 to 0.37, p<0.001; Nigeria OR 0.40, 95% CI 0.30 to 0.55, p<0.001). The odds of malaria were lower up to 2 months post-SMC in Burkina Faso (1-month post-SMC: OR 0.29, 95% CI 0.12 to 0.72, p=0.01; 2 months post-SMC: OR: 0.33, 95% CI 0.17 to 0.64, p<0.001). The odds of malaria were lower up to 1 month post-SMC in Nigeria but was not statistically significant (1-month post-SMC 0.49, 95% CI 0.23 to 1.05, p=0.07). A similar but weaker effect was seen for microscopy (Burkina Faso OR 0.38, 95% CI 0.29 to 0.52, p<0.001; Nigeria OR 0.53, 95% CI 0.38 to 0.76, p<0.001). CONCLUSIONS: Impact of SMC can be detected in reduced prevalence of malaria from data collected through household surveys if conducted during SMC administration or within 2 months afterwards. Such evidence could contribute to broader evaluation of impact of SMC programmes.
Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383−1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451−3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events.
BACKGROUND: Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns. METHODS: Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis. RESULTS: The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001). CONCLUSIONS: The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.
Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion - 0.25 (BCI: - 0.46, - 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.
A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.
BACKGROUND: Malaria is a serious public health problem of most developing countries, including Ethiopia. The burden of malaria is severely affecting the economy and lives of people, particularly among the productive ages of rural society. Thus, this study was targeted to analyze the past five-year retrospective malaria data among the rural setting of Maygaba town, Welkait district, northwest Ethiopia. METHODS: The study was done on 36,219 outpatients attending for malaria diagnosis during January 2015 to 2019. Data was extracted from the outpatient medical database. Chi-square (χ (2)) test and binary logistic regression model were used to analyze the retrospective data. Statistical significance was defined at p < 0.05. RESULTS: Of 36,219 outpatients examined, 7,309 (20.2%) malaria-positive cases were reported during 2015-2019. There was a fluctuating trend in the number of malaria-suspected and -confirmed cases in each year. Male slide-confirmed (61.4%, N = 4,485) were significantly higher than females (38.6%, N = 2,824) (p < 005). Plasmodium falciparum and Plasmodium vivax were the dominant parasites detected, which accounted for 66.1%; N = 4832, 33.9%; N = 2477, respectively. Despite the seasonal abundance of malaria cases, the highest prevalence was recorded in autumn (September to November) in the study area. Binary logistic regression analysis revealed that statistically significant associations were observed between sexes, interseasons, mean seasonal rainfall, and mean seasonal temperature with the prevalence of P. vivax. However, P. falciparum has shown a significant association with interseasons and mean seasonal temperature. CONCLUSIONS: Although the overall prevalence of malaria was continually declined from 2015-2019, malaria remains the major public health problem in the study area. The severe species of P. falciparum was found to be the dominant parasite reported in the study area. A collaborative action between the national malaria control program and its partners towards the transmission, prevention, and control of the two deadly species is highly recommended.
Background. In Sub-Saharan African countries, malaria is a leading cause of morbidity and mortality. In Ethiopia, malaria is found in three-fourths of its land mass with more than 63 million people living in malaria endemic areas. Nowadays, Ethiopia is implementing a malaria elimination program with the goal of eliminating the disease by 2030. To assist this goal, the trends of malaria cases should be evaluated with a function of time in different areas of the country to develop area-specific evidence-based interventions. Therefore, this study was aimed at analysing a five year trend of malaria in Nirak Health Center, Abergele District, Northeast Ethiopia, from 2016 to 2020. Methods. A retrospective study was conducted at Nirak Health Center, Abergele District, Northeast Ethiopia from February to April 2021. Five-year (2016 to 2020) retrospective data were reviewed from the malaria registration laboratory logbook. The sociodemographic and malaria data were collected using a predesigned data collection sheet. Data were entered, cleaned, and analysed using SPSS version 26. Results. In the five-year period, a total of 19,433 malaria suspected patients were diagnosed by microscopic examination. Of these, 6,473 (33.3%) were positive for malaria parasites. Of the total confirmed cases, 5,900 (91.2%) were P. falciparum and 474 (7.2%) were P. vivax. Majority of the cases were males (62.2%) and in the age group of 15-45 years old (52.8%). The findings of this study showed an increasing trend in malaria cases in the past five years (2016-2020). The maximum number of confirmed malaria cases reported was in the year 2020, while the minimum number of confirmed malaria cases registered was in 2016. Regarding the seasonal distribution of malaria, the highest number of malaria cases (55.2%) was observed in Dry season (September to January) and also the least (15.9%) was observed in Autumn (March to May) replaced by the least (21.6%) was observed in Rainy season (June to August), that is, the major malaria transmission season in Ethiopia and the least (15.9%) was observed in autumn (March to May). Conclusion. The trends of malaria in Nirak Health Center showed steadily increasing from the year 2016-2020, and the predominant species isolated was P. falciparum. This showed that the malaria control and elimination strategy in the area were not properly implemented or failed to achieve its designed goal. Therefore, this finding alarms the local governments and other stack holders urgently to revise their intervention strategies and take action in the locality.
BACKGROUND: Land use change has increasingly been expanding throughout the world in the past decades. It can have profound effects on the spatial and temporal distribution of vector borne diseases like malaria through ecological and habitat change. Understanding malaria disease occurrence and the impact of prevention interventions under this intense environmental modification is important for effective and efficient malaria control strategy. METHODS: A descriptive ecological study was conducted by reviewing health service records at Abobo district health office. The records were reviewed to extract data on malaria morbidity, mortality, and prevention and control methods. Moreover, Meteorological data were obtained from Gambella region Meteorology Service Center and National Meteorology Authority head office. Univariate, bivariate and multivariate analysis techniques were used to analyze the data. RESULTS: For the twelve-year time period, the mean annual total malaria case count in the district was 7369.58. The peak monthly malaria incidence was about 57 cases per 1000 people. Only in 2009 and 2015 that zero death due to malaria was recorded over the past 12 years. Fluctuating pattern of impatient malaria cases occurrence was seen over the past twelve years with an average number of 225.5 inpatient cases. The data showed that there is a high burden of malaria in the district. Plasmodium falciparum (Pf) was a predominant parasite species in the district with the maximum percentage of about 90. There was no statistically significant association between season and total malaria case number (F(3,8): 1.982, P:0.195). However, the inter-annual total case count difference was statistically significant (F(11,132): 36.305, p < 0001). Total malaria case count had shown two months lagged carry on effect. Moreover, 3 months lagged humidity had significant positive effect on total malaria cases. Malaria prevention interventions and meteorological factors showed statistically significant association with total malaria cases. CONCLUSION: Malaria was and will remain to be a major public health problem in the area. The social and economic impact of the disease on the local community is clearly pronounced as it is the leading cause of health facility visit and admission including the mortality associated with it. Scale up of effective interventions is quite important. Continuous monitoring of the performance of the vector control tools needs to be done.
BACKGROUND: Rainfall is one of the climate variables most studied as it affects malaria occurrence directly. OBJECTIVE: This study aimed to describe how monthly rainfall variability affects malaria incidence in different years. METHODS: A total of 7 years (2013/14-2019/20) retrospective confirmed and treated malaria cases in Gondar Zuria district were used for analysis in addition to five (2013/14-2017/18) years retrospective data from Dembia district. RESULTS: The annual rainfalls in the study years showed no statistically significant difference (p = 0. 78). But, variations in rainfalls of the different months (p = 0.000) of the different years were the source of variations for malaria count (incidences) in the different years. Malaria was transmitted throughout the year with the highest peak in November (mean count = 1468.7 ± 697.8) and followed by May (mean count = 1253.4 ± 1391.8), after main Kiremt/Summer and minor Bulg/Spring rains respectively. The lowest transmission was occurred in February (338 ± 240.3) when the rivers were the only source of mosquito vectors. Year 2013/14 (RF = 2351.12 mm) and 2019/20 (RF = 2278.80 mm) with no statistically significant difference (p = 0.977) in annual rainfalls produced 10, 702 (49.2%) and 961 (20%) malaria counts for the Bulg (spring) season respectively due to 581.92 mm (24.8%) higher total Bulg/Spring rain in 2013/14 compared to 124.1 mm (5.45%) in 2019/20. Generally, above normal rainfalls in Bulg/Spring season increased malaria transmission by providing more aquatic habitats supporting the growth of the immature stages. But heavy rains in Summer/Kiremt produced low malaria counts due to the high intensity of the rainfalls which could kill the larvae and pupae. Spearman’s correlation analysis indicated that the mean rainfalls of current month (RF) (0 lagged month) (P = 0.025), previous month (RF1) (1 month lagged) (p = 0.000), before previous months (RF2) (2 months lagged) (p = 0.001) and mean RF + RF1 + RF2 (P = 0.001) were positive significantly correlated with mean monthly malaria counts compared to negative significant correlations for temperature variables. Temperature variables negative correlations were interpreted as confounding effects because decreased malaria counts in dry months were due to a decrease in rainfalls. Conclusion: rainfall distribution in different months of a year affects malaria occurrences.
BACKGROUND: Current literatures seem devoted only on relating climate change with malaria. Overarching all possible environmental determinants of malaria prevalence addressed by scanty literature in Nepal is found apposite research at this moment. This study aims to explore the environmental determinants of malaria prevalence in western Nepal. METHODS: Cross-sectional data collected from community people were used to identify the environmental determinants of malaria prevalence in western Nepal. Probit and logistic regressions are used for identifying determinants. RESULTS: The results reveal that environmental variables: winter temperature (aOR: 2.14 [95% CI: 1.00-4.56]), flooding (aOR: 2.45 [CI: 1.28-4.69]), heat waves (aOR: 3.14 [CI: 1.16-8.46]) and decreasing river water level (aOR: 0.25 [CI: 0.13-0.47]) are found major factors to influence malaria prevalence in western Nepal. Besides, pipeline drinking water (aOR: 0.37 [0.17-0.81]), transportation facility (aOR: 1.18 [1.07-1.32]) and awareness programs (aOR: 2.62 [0.03-6.65]) are exigent social issues to influence malaria prevalence in Nepal. To be protected from disease induced by environmental problems, households have used extra season specific clothes, iron nets and mosquito nets, use of insecticide in cleaning toilet and so on. CONCLUSIONS: Adaptation mechanism against these environmental issues together with promoting pipeline drinking water, transportation facility and awareness programs are the important in malaria control in Nepal. Government initiation with incentivized adaptation mechanism for the protection of environment with caring household attributes possibly help control malaria in western Nepal.
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.
Meeting global and national malaria elimination targets requires identifying challenges as early as possible so that strategies can be modified to stay on track. This qualitative study of stakeholders who have a major influence on malaria programs across the Southeast Asian region, including those at a state level in India and at a national level in Cambodia, Myanmar, Thailand and Vietnam, shows that most believe Plasmodium falciparum malaria elimination targets are attainable, but are less optimistic for meeting Plasmodium vivax targets. Across these countries, stakeholders reported large variations in access to malaria diagnosis and treatment; the effectiveness of strategies for reaching migrants and hardto-serve populations; and securing sufficient numbers of skilled workers for both diagnosis and compliance with artemisinin-combination treatments and the need to optimise use of insecticides. Additionally, there was optimism about coordinated surveillance and response, but this was counterbalanced with a sense that national and regional collaboration opportunities have been missed. Climate change impacts were seen as a potential threat by all stakeholders in this study and in need of further research.
BACKGROUND: Climate and climate change affect the spatial pattern and seasonality of malaria risk. Season lengths and spatial extents of mapped current and future malaria transmission suitability predictions for Nepal were assessed for a combination of malaria vector and parasites: Anopheles stephensi and Plasmodium falciparum (ASPF) and An. stephensi and Plasmodium vivax (ASPV) and compared with observed estimates of malaria risk in Nepal. METHODS: Thermal bounds of malaria transmission suitability for baseline (1960-1990) and future climate projections (RCP 4.5 and RCP 8.5 in 2030 and 2050) were extracted from global climate models and mapped for Nepal. Season length and spatial extent of suitability between baseline and future climate scenarios for ASPF and ASPV were compared using the Warren’s I metric. Official 2010 DoHS risk districts (DRDs) and 2021 DoHS risk wards (DRWs), and spatiotemporal incidence trend clusters (ITCs) were overlaid on suitability season length and extent maps to assess agreement, and potential mismatches. RESULTS: Shifts in season length and extent of malaria transmission suitability in Nepal are anticipated under both RCP 4.5 and RCP 8.5 scenarios in 2030 and 2050, compared to baseline climate. The changes are broadly consistent across both future climate scenarios for ASPF and ASPV. There will be emergence of suitability and increasing length of season for both ASPF and ASPV and decreasing length of season for ASPV by 2050. The emergence of suitability will occur in low and no-risk DRDs and outside of high and moderate-risk DRWs, season length increase will occur across all DRD categories, and outside of high and moderate-risk DRWs. The high and moderate risk DRWs of 2021 fall into ITCs with decreasing trend. CONCLUSIONS: The study identified areas of Nepal where malaria transmission suitability will emerge, disappear, increase, and decrease in the future. However, most of these areas are anticipated outside of the government’s current and previously designated high and moderate-risk areas, and thus outside the focus of vector control interventions. Public health officials could use these anticipated changing areas of malaria risk to inform vector control interventions for eliminating malaria from the country, and to prevent malaria resurgence.
BACKGROUND/OBJECTIVE: Natural disasters (NDs) are calamitous phenomena that can increase the risk of infections in disaster-affected regions. This study aimed to evaluate the frequency of malaria and cutaneous leishmaniasis (CL) before and after earthquakes, floods, and droughts during the past four decades in Iran. METHODS: Malaria and CL data were obtained from the reports of the Ministry of Health and Medical Education in Iran for the years 1983 through 2017. The data of NDs were extracted from the Centre for Research on the Epidemiology of Disasters (CRED). Interrupted time series analysis with linear regression modeling was used to estimate time trends of mentioned diseases in pre- and post-disaster conditions. RESULTS: For the periods preceding the disasters drought and flood, a decreasing time trend for malaria and CL was found over time. The time trend of malaria rate preceding the 1990 earthquake was stable, a downward trend was found after 1990 disaster until 1997 (β coefficient: -10.7; P = .001), and this declining trend was continued after 1997 disaster (β coefficient: -2.7; P = .001). The time trend of CL rate preceding the 1990 earthquake had a declining trend, an upward trend was found after 1990 earthquake until 1999 (β coefficient: +8.7; P = .293), and a slight upward trend had also appeared after 1999 earthquake (β coefficient: +0.75; P = .839). CONCLUSION: The results of the current study indicated the occurrence of earthquakes, floods, and droughts has no significant effect on the frequency of malaria and CL in Iran.
BACKGROUND: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates. METHODS: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR). RESULTS: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar-e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92-2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03-1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90-0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island. CONCLUSION: Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.
In Brazil, approximately 99% of malaria cases are concentrated in the Amazon region. An acute febrile infectious disease, malaria is closely related to climatic and hydrological factors. Environmental variables such as rainfall, flow, level, and color of rivers, the latter associated with the suspended sediment concentration, are important factors that can affect the dynamics of the incidence of some infectious diseases, including malaria. This study explores the possibility that malaria incidence is influenced by precipitation, fluctuations in river levels, and suspended sediment concentration. The four studied municipalities are located in two Brazilian states (Amazonas and Para) on the banks of rivers with different hydrological characteristics. The results suggest that precipitation and river level fluctuations modulate the seasonal pattern of the disease and evidence the existence of delayed effects of river floods on malaria incidence. The seasonality of the disease has a different influence in each municipality studied. However, municipalities close to rivers with the same characteristic color of waters (as a function of the concentration of suspended sediments) have similar responses to the disease.
The threats, both real and perceived, surrounding the development of new and emerging infectious diseases of humans are of critical concern to public health and well-being. Among these risks is the potential for zoonotic transmission to humans of species of the malaria parasite, Plasmodium, that have been considered historically to infect exclusively non-human hosts. Recently observed shifts in the mode, transmission, and presentation of malaria among several species studied are evidenced by shared vectors, atypical symptoms, and novel host-seeking behavior. Collectively, these changes indicate the presence of environmental and ecological pressures that are likely to influence the dynamics of these parasite life cycles and physiological make-up. These may be further affected and amplified by such factors as increased urban development and accelerated rate of climate change. In particular, the extended host-seeking behavior of what were once considered non-human malaria species indicates the specialist niche of human malaria parasites is not a limiting factor that drives the success of blood-borne parasites. While zoonotic transmission of non-human malaria parasites is generally considered to not be possible for the vast majority of Plasmodium species, failure to consider the feasibility of its occurrence may lead to the emergence of a potentially life-threatening blood-borne disease of humans. Here, we argue that recent trends in behavior among what were hitherto considered to be non-human malaria parasites to infect humans call for a cross-disciplinary, ecologically-focused approach to understanding the complexities of the vertebrate host/mosquito vector/malaria parasite triangular relationship. This highlights a pressing need to conduct a multi-species investigation for which we recommend the construction of a database to determine ecological differences among all known Plasmodium species, vectors, and hosts. Closing this knowledge gap may help to inform alternative means of malaria prevention and control.
Two recent initiatives, the World Health Organization (WHO) Strategic Advisory Group on Malaria Eradication and the Lancet Commission on Malaria Eradication, have assessed the feasibility of achieving global malaria eradication and proposed strategies to achieve it. Both reports rely on a climate-driven model of malaria transmission to conclude that long-term trends in climate will assist eradication efforts overall and, consequently, neither prioritize strategies to manage the effects of climate variability and change on malaria programming. This review discusses the pathways via which climate affects malaria and reviews the suitability of climate-driven models of malaria transmission to inform long-term strategies such as an eradication programme. Climate can influence malaria directly, through transmission dynamics, or indirectly, through myriad pathways including the many socioeconomic factors that underpin malaria risk. These indirect effects are largely unpredictable and so are not included in climate-driven disease models. Such models have been effective at predicting transmission from weeks to months ahead. However, due to several well-documented limitations, climate projections cannot accurately predict the medium- or long-term effects of climate change on malaria, especially on local scales. Long-term climate trends are shifting disease patterns, but climate shocks (extreme weather and climate events) and variability from sub-seasonal to decadal timeframes have a much greater influence than trends and are also more easily integrated into control programmes. In light of these conclusions, a pragmatic approach is proposed to assessing and managing the effects of climate variability and change on long-term malaria risk and on programmes to control, eliminate and ultimately eradicate the disease. A range of practical measures are proposed to climate-proof a malaria eradication strategy, which can be implemented today and will ensure that climate variability and change do not derail progress towards eradication.
The rearing temperature of the immature stages can have a significant impact on the life-history traits and the ability of adult mosquitoes to transmit diseases. This review assessed published evidence of the effects of temperature on the immature stages, life-history traits, insecticide susceptibility, and expression of enzymes in the adult Anopheles mosquito. Original articles published through 31 March 2021 were systematically retrieved from Scopus, Google Scholar, Science Direct, PubMed, ProQuest, and Web of Science databases. After applying eligibility criteria, 29 studies were included. The review revealed that immature stages of An. arabiensis were more tolerant (in terms of survival) to a higher temperature than An. funestus and An. quadriannulatus. Higher temperatures resulted in smaller larval sizes and decreased hatching and pupation time. The development rate and survival of An. stephensi was significantly reduced at a higher temperature than a lower temperature. Increasing temperatures decreased the longevity, body size, length of the gonotrophic cycle, and fecundity of Anopheles mosquitoes. Higher rearing temperatures increased pyrethroid resistance in adults of the An. arabiensis SENN DDT strain, and increased pyrethroid tolerance in the An. arabiensis SENN strain. Increasing temperature also significantly increased Nitric Oxide Synthase (NOS) expression and decreased insecticide toxicity. Both extreme low and high temperatures affect Anopheles mosquito development and survival. Climate change could have diverse effects on Anopheles mosquitoes. The sensitivities of Anopeheles mosquitoes to temperature differ from species to species, even among the same complex. Notwithstanding, there seem to be limited studies on the effects of temperature on adult life-history traits of Anopheles mosquitoes, and more studies are needed to clarify this relationship.
One of the most important vector-borne disease in humans is malaria, caused by Plasmodium parasite. Seasonal temperature elements have a major effect on the life development of mosquitoes and the development of parasites. In this paper, we establish and analyze a reaction-diffusion model, which includes seasonality, vector-bias, temperature-dependent extrinsic incubation period (EIP) and maturation delay in mosquitoes. In order to get the model threshold dynamics, a threshold parameter, the basic reproduction number R-0 is introduced, which is the spectral radius of the next generation operator. Quantitative analysis indicates that when R-0 < 1, there is a globally attractive disease-free omega-periodic solution; disease is uniformly persistent in humans and mosquitoes if R-0 > 1. Numerical simulations verify the results of the theoretical analysis and discuss the effects of diffusion and seasonality. We study the relationship between the parameters in the model and R-0. More importantly, how to allocate medical resources to reduce the spread of disease is explored through numerical simulations. Last but not least, we discover that when studying malaria transmission, ignoring vector-bias or assuming that the maturity period is not affected by temperature, the risk of disease transmission will be underestimate.
This paper mainly explores the complex impacts of spatial heterogeneity, vector-bias effect, multiple strains, temperature-dependent extrinsic incubation period (EIP) and seasonality on malaria transmission. We propose a multi-strain malaria transmission model with diffusion and periodic delays and define the reproduction numbers Ri and R^i (i = 1, 2). Quantitative analysis indicates that the disease-free ω-periodic solution is globally attractive when Ri < 1, while if Ri > 1 > Rj (i ≠ j, i, j = 1, 2), then strain i persists and strain j dies out. More interestingly, when R1 and R2 are greater than 1, the competitive exclusion of the two strains also occurs. Additionally, in a heterogeneous environment, the coexistence conditions of the two strains are R^1 > 1 and R^2 > 1. Numerical simulations verify the analytical results and reveal that ignoring vector-bias effect or seasonality when studying malaria transmission will underestimate the risk of disease transmission.
As a long-standing public health issue, malaria still severely affects many parts of the world, especially Africa. With greenhouse gas emissions, temperatures continue to rise. Based on diverse shared socioeconomic pathways (SSPs), future temperatures can be estimated. However, the impacts of climate change on malaria infection rates in all epidemic regions are unknown. Here, we estimate the differences in global malaria infection rates predicted under different SSPs during several periods as well as malaria infection case changes (MICCs) resulting from those differences. Our results indicate that the global MICCs resulting from the conversion from SSP1-2.6 to SSP2-4.5, to SSP3-7.0, and to SSP5-8.5 are 6.506 (with a 95% uncertainty interval [UI] of 6.150-6.861) million, 3.655 (3.416-3.894) million, and 2.823 (2.635-3.012) million, respectively, from 2021 to 2040; these values represent increases of 2.699%, 1.517%, and 1.171%, respectively, compared to the 241 million infection cases reported in 2020. Temperatures increases will adversely affect malaria the most in Africa during the 2021-2040 period. From 2081 to 2100, the MICCs obtained for the three scenario shifts listed above are -79.109 (-83.626 to -74.591) million, -238.337 (-251.920 to -0.141) million, and -162.692 (-174.628 to -150.757) million, corresponding to increases of -32.825%, -98.895%, and -67.507%, respectively. Climate change will increase the danger and risks associated with malaria in the most vulnerable regions in the near term, thus aggravating the difficulty of eliminating malaria. Reducing GHG emissions is a potential pathway to protecting people from malaria.
BACKGROUND: Climate change is expected to alter the global footprint of many infectious diseases, particularly vector-borne diseases such as malaria and dengue. Knowledge of the range and geographical context of expected climate change impacts on disease transmission and spread, combined with knowledge of effective adaptation strategies and responses, can help to identify gaps and best practices to mitigate future health impacts. To investigate the types of evidence for impacts of climate change on two major mosquito-borne diseases of global health importance, malaria and dengue, and to identify the range of relevant policy responses and adaptation strategies that have been devised, we performed a scoping review of published review literature. Three electronic databases (PubMed, Scopus and Epistemonikos) were systematically searched for relevant published reviews. Inclusion criteria were: reviews with a systematic search, from 2007 to 2020, in English or French, that addressed climate change impacts and/or adaptation strategies related to malaria and/or dengue. Data extracted included: characteristics of the article, type of review, disease(s) of focus, geographic focus, and nature of the evidence. The evidence was summarized to identify and compare regional evidence for climate change impacts and adaptation measures. RESULTS: A total of 32 reviews met the inclusion criteria. Evidence for the impacts of climate change (including climate variability) on dengue was greatest in the Southeast Asian region, while evidence for the impacts of climate change on malaria was greatest in the African region, particularly in highland areas. Few reviews explicitly addressed the implementation of adaptation strategies to address climate change-driven disease transmission, however suggested strategies included enhanced surveillance, early warning systems, predictive models and enhanced vector control. CONCLUSIONS: There is strong evidence for the impacts of climate change, including climate variability, on the transmission and future spread of malaria and dengue, two of the most globally important vector-borne diseases. Further efforts are needed to develop multi-sectoral climate change adaptation strategies to enhance the capacity and resilience of health systems and communities, especially in regions with predicted climatic suitability for future emergence and re-emergence of malaria and dengue. This scoping review may serve as a useful precursor to inform future systematic reviews of the primary literature.
BACKGROUND: Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users’ perspective of their applications. METHODS: Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FINDINGS: Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users’ perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. CONCLUSIONS: In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.
BACKGROUND: Understanding of the impacts of climatic variability on human health remains poor despite a possibly increasing burden of vector-borne diseases under global warming. Numerous socioeconomic variables make such studies challenging during the modern period while studies of climate-disease relationships in historical times are constrained by a lack of long datasets. Previous studies have identified the occurrence of malaria vectors, and their dependence on climate variables, during historical times in northern Europe. Yet, malaria in Sweden in relation to climate variables is understudied and relationships have never been rigorously statistically established. This study seeks to examine the relationship between malaria and climate fluctuations, and to characterise the spatio-temporal variations at parish level during severe malaria years in Sweden 1749-1859. METHODS: Symptom-based annual malaria case/death data were obtained from nationwide parish records and military hospital records in Stockholm. Pearson (r(p)) and Spearman’s rank (r(s)) correlation analyses were conducted to evaluate inter-annual relationship between malaria data and long meteorological series. The climate response to larger malaria events was further explored by Superposed Epoch Analysis, and through Geographic Information Systems analysis to map spatial variations of malaria deaths. RESULTS: The number of malaria deaths showed the most significant positive relationship with warm-season temperature of the preceding year. The strongest correlation was found between malaria deaths and the mean temperature of the preceding June-August (r(s) = 0.57, p < 0.01) during the 1756-1820 period. Only non-linear patterns can be found in response to precipitation variations. Most malaria hot-spots, during severe malaria years, concentrated in areas around big inland lakes and southern-most Sweden. CONCLUSIONS: Unusually warm and/or dry summers appear to have contributed to malaria epidemics due to both indoor winter transmission and the evidenced long incubation and relapse time of P. vivax, but the results also highlight the difficulties in modelling climate-malaria associations. The inter-annual spatial variation of malaria hot-spots further shows that malaria outbreaks were more pronounced in the southern-most region of Sweden in the first half of the nineteenth century compared to the second half of the eighteenth century.
BACKGROUND: Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear. METHODS: Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes. RESULTS: Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%-9.6%). CONCLUSIONS: Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.
BACKGROUND: In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. METHODS: In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. RESULTS: From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. CONCLUSIONS: A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.
Environmental temperature is a key driver of malaria transmission dynamics. Using detailed temperature records from four sites: low elevation (1800), mid elevation (2200 m), and high elevation (2600-3200 m) in the western Himalaya, we model how temperature regulates parasite development rate (the inverse of the extrinsic incubation period, EIP) in the wild. Using a Briére parametrization of the EIP, combined with Bayesian parameter inference, we study the thermal limits of transmission for avian (Plasmodium relictum) and human Plasmodium parasites (P. vivax and P. falciparum) as well as for two malaria-like avian parasites, Haemoproteus and Leucocytozoon. We demonstrate that temperature conditions can substantially alter the incubation period of parasites at high elevation sites (2600-3200 m) leading to restricted parasite development or long transmission windows. The thermal limits (optimal temperature) for Plasmodium parasites were 15.62-34.92°C (30.04°C) for P. falciparum, 13.51-34.08°C (29.02°C) for P. vivax, 12.56-34.46°C (29.16°C) for P. relictum and for two malaria-like parasites, 12.01-29.48°C (25.16°C) for Haemoproteus spp. and 11.92-29.95°C (25.51°C) for Leucocytozoon spp. We then compare estimates of EIP based on measures of mean temperature versus hourly temperatures to show that EIP days vary in cold versus warm environments. We found that human Plasmodium parasites experience a limited transmission window at 2600 m. In contrast, for avian Plasmodium transmission was not possible between September and March at 2600 m. In addition, temperature conditions suitable for both Haemoproteus and Leucocytozoon transmission were obtained from June to August and in April, at 2600 m. Finally, we use temperature projections from a suite of climate models to predict that by 2040, high elevation sites (~2600 m) will have a temperature range conducive for malaria transmission, albeit with a limited transmission window. Our study highlights the importance of accounting for fine-scale thermal effects in the expansion of the range of the malaria parasite with global climate change.
Malaria elimination is a global priority, which India has also adopted as a target. Despite the malaria control efforts like long-lasting insecticidal nets distribution, rounds of indoor residual spray, the introduction of bi-valent rapid diagnostic tests and artemisinin combination therapy, malaria remained consistent in Dolonibasti sub-center of Orang block primary health center (BPHC) under the district Udalguri, Assam state followed by abrupt rise in cases in 2018. Therefore, we aimed to investigate the factors driving the malaria transmission in the outbreak area of Dolonibasti sub-center. Malaria epidemiological data (2008-2018) of Udalguri district and Orang BPHC was collected. The annual (2011-2018) and monthly (2013-2018) malaria and meteorological data of Dolonibasti sub-center was collected. An entomological survey, Knowledge, Attitude and Practices study among malaria cases (n = 120) from Dolonibasti was conducted. In 2018, 26.1 % (2136/ 8188) of the population of Dolonibasti were found to be malaria positive, of which 55% were adults (n = 1176). Majority of cases were from tea tribe populations (90%), either asymptomatic or with fever only, 67.5 % (81/120) had experienced malaria infection during past years. The outbreak was characterized by a strong increase in cases in June 2018, high proportion of slide falciparum rate of 26.1% (other years average, 15.8%) and high proportion of P. falciparum of 81.2 % (other years average, 84.3%). Anopheles minimus s.l. was the major vector with 28.6% positivity and high larval density in paddy fields/ drainage area. Annual relative humidity was associated with rise in malaria cases, annual parasite incidence (r(s) = 0.69, 90%CI; p = 0.06) and slide positivity rate (r(s) = 0.83, 95%CI; p = 0.01). Older people were less educated (r(s) = -0.66; p < 0.001), had lesser knowledge about malaria cause (r(s) = -0.42; χ(2)=21.80; p < 0.001) and prevention (r(s) = -0.18; p = 0.04). Malaria control practices were followed by those having knowledge about cause of malaria (r(s) = 0.36; χ(2) = 13.50; p < 0.001) and prevention (r(s) = 0.40; χ(2) = 17.71; p < 0.001). Altogether, 84.6% (44/52) of the respondents did not use protective measures. We described a sudden increase in malaria incidence in a rural, predominantly tea tribe population group with high illiteracy rate and ignorance on protective measures against malaria. More efforts that are concerted needed to educate the community about malaria control practices.
BACKGROUND: Malaria is one of the most life-threatening vector-borne diseases globally. Recent autochthonous cases registered in several European countries have raised awareness regarding the threat of malaria reintroduction to Europe. An increasing number of imported malaria cases today occur due to international travel and migrant flows from malaria-endemic countries. The cumulative factors of the presence of competent vectors, favourable climatic conditions and evidence of increasing temperatures might lead to the re-emergence of malaria in countries where the infection was previously eliminated. METHODS: We performed a systematic literature review following PRISMA guidelines. We searched for original articles focusing on rising temperature and the receptivity to malaria transmission in Europe. We evaluated the quality of the selected studies using a standardised tool. RESULTS: The search resulted in 1’999 articles of possible relevance and after screening we included 10 original research papers in the quantitative analysis for the systematic review. With further increasing temperatures studies predicted a northward spread of the occurrence of Anopheles mosquitoes and an extension of seasonality, enabling malaria transmission for annual periods up to 6 months in the years 2051-2080. Highest vector stability and receptivity were predicted in Southern and South-Eastern European areas. Anopheles atroparvus, the main potential malaria vector in Europe, might play an important role under changing conditions favouring malaria transmission. CONCLUSION: The receptivity of Europe for malaria transmission will increase as a result of rising temperature unless socioeconomic factors remain favourable and appropriate public health measures are implemented. Our systematic review serves as an evidence base for future preventive measures.
Efforts have been made to quantify the spatio-temporal malaria transmission intensity over India using the dynamical malaria model, namely, Vector-borne Disease Community Model of International Centre for Theoretical Physics Trieste (VECTRI). The likely effect of climate change in the variability of malaria transmission intensity over different parts of India is also investigated. The Historical data and future projection scenarios of the rainfall and temperature derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model output are used for this purpose. The Entomological Inoculation Rate (EIR) and Vector are taken as quantifiers of malaria transmission intensity. It is shown that the maximum number of malaria cases over India occur during the Sept-Oct months, whereas the minimum during the Feb-Apr months. The malaria transmission intensity as well as length of transmission season over India is likely to increase in the future climate as a result of global warming.
Understanding the regional impact of future climate change is one of the major global challenges of this century. This study investigated possible effects of climate change on malaria in West Africa in the near future (2006-2035) and the far future (2036-2065) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), compared to an observed evaluation period (1981-2010). Projected rainfall and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). The malaria model used is the Liverpool malaria model (LMM), a dynamical malaria model driven by daily time series of rainfall and temperature obtained from the CORDEX data. Our results highlight the unimodal shape of the malaria prevalence distribution, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall. Projections showed that the mean annual malaria prevalence would decrease in both climatological periods under both RCPs but with a larger magnitude of decreasing under the RCP8.5. We found that the mean malaria prevalence for the reference period is greater than the projected prevalence for 6 of the 8 downscaled GCMs. The study enhances understanding of how malaria is impacted under RCP4.5 and RCP8.5 emission scenarios. These results indicate that the southern area of West Africa is at most risk of epidemics, and the malaria control programs need extra effort and help to make the best use of available resources by stakeholders.
Malaria is a climate-sensitive infectious disease. Many ecological studies have investigated the independent impacts of ambient temperature on malaria. However, the optimal temperature measures of malaria and its interaction with other meteorological factors on malaria transmission are less understood. This study aims to investigate the effect of ambient temperature and its interactions with relative humidity and rainfall on malaria in Suzhou, a temperate climate city in Anhui Province, China. Weekly malaria and meteorological data from 2005 to 2012 were obtained for Suzhou. A distributed lag nonlinear model was conducted to quantify the effect of different temperature measures on malaria. The best measure was defined as that with the minimum quasi-Akaike information criterion. GeoDetector and Poisson regression models were employed to quantify the interactions of temperature, relative humidity, and rainfall on malaria transmission. A total of 13,382 malaria cases were notified in Suzhou from 2005 to 2012. Each 5 °C rise in average temperature over 10 °C resulted in a 22% (95% CI: 17%, 28%) increase in malaria cases at lag of 4 weeks. In terms of cumulative effects from lag 1 to 8 weeks, each 5 °C increase over 10 °C caused a 175% growth in malaria cases (95% CI: 139%, 216%). Average temperature achieved the best performance in terms of model fitting, followed by minimum temperature, most frequent temperature, and maximum temperature. Temperature had an interactive effect on malaria with relative humidity and rainfall. High temperature together with high relative humidity and high rainfall could accelerate the transmission of malaria. Meteorological factors may affect malaria transmission interactively. The research findings could be helpful in the development of weather-based malaria early warning system, especially in the context of climate change for the prevention of possible malaria resurgence.
Since the early twentieth century, the intensity of malaria transmission has decreased sharply worldwide, although it is still an infectious disease with a yearly estimate of 228 million cases. The aim of this study was to expand our knowledge on the main drivers of malaria in Spain. In the case of autochthonous malaria, these drivers were linked to socioeconomic and hygienic and sanitary conditions, especially in rural areas due to their close proximity to the wetlands that provide an important habitat for anopheline reproduction. In the case of imported malaria, the main drivers were associated with urban areas, a high population density and international communication nodes (e.g. airports). Another relevant aspect is that the major epidemic episodes of the twentieth century were strongly influenced by war and military conflicts and overcrowding of the healthcare system due to the temporal overlap with the pandemic flu of 1918. Therefore, military conflicts and overlap with other epidemics or pandemics are considered to be drivers of malaria that can-in a temporary manner-exponentially intensify transmission of the disease. Climatic factors did not play a relevant role as drivers of malaria in Spain (at least directly). However, they did influence the seasonality of the disease and, during the epidemic outbreak of 1940-1944, the climate conditions favored or coadjuvated its spread. The results of this study provide additional knowledge on the seasonal and interannual variability of malaria that can help to develop and implement health risk control measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41207-021-00245-8.
Around 27% of South Americans live in central and southern Brazil. Of 19,400 human malaria cases in Brazil in 2018, some were from the southern and southeastern states. High abundance of malaria vectors is generally positively associated with malaria incidence. Expanding geographic distributions of Anopheles vector mosquito species (e.g. A. cruzii) in the face of climate change processes would increase risk of such malaria transmission; such risk is of particular concern in regions that hold human population concentrations near present limits of vector species’ geographic distributions. We modeled effects of likely climate changes on the distribution of A. cruzii, evaluating two scenarios of future greenhouse gas emissions for 2050, as simulated in 21 general circulation models and two greenhouse gas scenarios (RCP 4.5 and RCP 8.5) for 2050. We tested 1305 candidate models, and chose among them based on statistical significance, predictive performance, and complexity. The models closely approximated the known geographic distribution of the species under current conditions. Under scenarios of future climate change, we noted increases in suitable area for the mosquito vector species in São Paulo and Rio de Janeiro states, including areas close to 30 densely populated cities. Under RCP 8.5, our models anticipate areal increases of >75% for this important malaria vector in the vicinity of 20 large Brazilian cities. We developed models that anticipate increased suitability for the mosquito species; around 50% of Brazilians reside in these areas, and ?89% of foreign tourists visit coastal areas in this region. Under climate change thereefore, the risk and vulnerability of human populations to malaria transmission appears bound to increase.
BACKGROUND: In the Republic of Congo, hot temperature and seasons distortions observed may impact the development of malaria parasites. We investigate the variation of malaria cases, parasite density and the multiplicity of Plasmodium falciparum infection throughout the year in Brazzaville. METHODS: From May 2015 to May 2016, suspected patients with uncomplicated malaria were enrolled at the Hôpital de Mfilou, CSI « Maman Mboualé», and the Laboratoire National de Santé Publique. For each patient, thick blood was examined and parasite density was calculated. After DNA isolation, MSP1 and MSP2 genes were genotyped. RESULTS: A total of 416, 259 and 131 patients with suspected malaria were enrolled at the CSI «Maman Mboualé», Hôpital de Mfilou and the Laboratoire National de Santé Publique respectively. Proportion of malaria cases and geometric mean parasite density were higher at the CSI «Maman Mboualé» compared to over sites (P-value <0.001). However the multiplicity of infection was higher at the Hôpital de Mfilou (P-value <0.001). At the Laboratoire National de Santé Publique, malaria cases and multiplicity of infection were not influenced by different seasons. However, variation of the mean parasite density was statistically significant (P-value <0.01). Higher proportions of malaria cases were found at the end of main rainy season either the beginning of the main dry season at the Hôpital de Mfilou and the CSI «Maman Mboualé»; while, lowest proportions were observed in September and January and in September and March respectively. Higher mean parasite densities were found at the end of rainy seasons with persistence at the beginning of dry seasons. The lowest mean parasite densities were found during dry seasons, with persistence at the beginning of rainy seasons. Fluctuation of the multiplicity of infection throughout the year was observed without significance between seasons. CONCLUSION: The current study suggests that malaria transmission is still variable between the north and south parts of Brazzaville. Seasonal fluctuations of malaria cases and mean parasite densities were observed with some extension to different seasons. Thus, both meteorological and entomological studies are needed to update the season's periods as well as malaria transmission intensity in Brazzaville.
Malaria is a vector-borne disease of significant public health concern. Despite widespread success of many elimination initiatives, elimination efforts in some regions of the world have stalled. Barriers to malaria elimination include climate and land use changes, such as warming temperatures and urbanization, which can alter mosquito habitats. Socioeconomic factors, such as political instability and regional migration, also threaten elimination goals. This is particularly relevant in areas where local elimination has been achieved and consequently surveillance and control efforts are dwindling and are no longer a priority. Understanding how environmental change, impacts malaria elimination has important practical implications for vector control and disease surveillance strategies. It is important to consider climate change when monitoring the threat of malaria resurgence due to socioeconomic influences. However, there is limited assessment of how the combination of climate variation, interventions and socioeconomic pressures influence long-term trends in malaria transmission and elimination efforts. In this study, we used Bayesian hierarchical mixed models and malaria case data for a 29-year period to disentangle the impacts of climate variation and malaria control efforts on malaria risk in the Ecuadorian province of El Oro, which achieved local elimination in 2011. We found shifting patterns of malaria between rural and urban areas, with a relative increase ofPlasmodium vivaxin urbanized areas. Minimum temperature was an important driver of malaria seasonality and the association between warmer minimum temperatures and malaria incidence was greater forPlasmodium falciparumcompared toP. vivaxmalaria. There was considerable heterogeneity in the impact of three chemical vector control measures on bothP. falciparumandP. vivaxmalaria. We found statistically significant associations between two of the three measures [indoor residual spraying (IRS) and space spraying] and a reduction in malaria incidence, which varied between malaria type. We also found environmental suitability for malaria transmission is increasing in El Oro, which could limit future elimination efforts if malaria is allowed to re-establish. Our findings have important implications for understanding environmental obstacles to malaria elimination and highlights the importance of designing and sustaining elimination efforts in areas that remain vulnerable to resurgence.
Previous studies that observed the fact that Middle Palaeolithic sites mainly were concentrated in arid and semi-arid areas in Africa and Southwest Asia, concluded that climate factors determined the distribution patterns. We argue that biological factors could have been equally important. In present-day sub-Saharan Africa, mosquito borne diseases and especially falciparum malaria have a serious impact on human populations. This study was aimed to investigate the possible former effect of falciparum malaria on Middle Palaeolithic site distribution patterns and explain why ancient humans avoided the humid areas in the tropical and subtropical regions. It was found that the early human settlements situated in those regions of Africa and Southwest Asia where the potential annual development period of falciparum parasites was short in the mosquitoes, the area was not too humid, and the potential falciparum malaria incidence values were low or moderate. In the Indian Peninsula, precipitation played a less significant role in determining human settlements. The number of the months when the extrinsic development of Plasmodium falciparum parasites was possible showed the strongest structural overlap with the modelled malaria incidences according to the spatial occurrence of the Middle Paleolithic archaeological sites in the case of Africa and in Southwest Asia. In the Indian Peninsula, climatic factors showed the strongest structural overlap with the modelled malaria incidences according to the occurrence patterns of the Middle Palaeolithic archaeological sites.
Objective: To determine the significance of temperature, rainfall and humidity in the seasonal abundance of Anopheles stephensi in southern Iran. Methods: Data on the monthly abundance of Anopheles stephensi larvae and adults were gathered from earlier studies conducted between 2002 and 2019 in malaria prone areas of southeastern Iran. Climatic data for the studied counties were obtained from climatology stations. Generalized estimating equations method was used for cluster correlation of data for each study site in different years. Results: A significant relationship was found between monthly density of adult and larvae of Anopheles stephensi and precipitation, max temperature and mean temperature, both with simple and multiple generalized estimating equations analysis (P<0.05). But when analysis was done with one month lag, only relationship between monthly density of adults and larvae of Anopheles stephensi and max temperature was significant (P<0.05). Conclusions: This study provides a basis for developing multivariate time series models, which can be used to develop improved appropriate epidemic prediction systems for these areas. Long-term entomological study in the studied sites by expert teams is recommended to compare the abundance of malaria vectors in the different areas and their association with climatic variables.
BACKGROUND: As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. METHODS: Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. RESULTS: An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I?=?0.3 (p <?0.001) and districts Moran’s I?=?0.4 (p <?0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. CONCLUSION: Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.
BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR?=?0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.
Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June-August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005-1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928-0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.
BACKGROUND: Malaria continues to be a disease of massive burden in Africa, and the public health resources targeted at surveillance, prevention, control, and intervention comprise large outlays of expense. Malaria transmission is largely constrained by the suitability of the climate for Anopheles mosquitoes and Plasmodium parasite development. Thus, as climate changes, shifts in geographic locations suitable for transmission, and differing lengths of seasons of suitability will occur, which will require changes in the types and amounts of resources. METHODS: The shifting geographic risk of malaria transmission was mapped, in context of changing seasonality (i.e. endemic to epidemic, and vice versa), and the number of people affected. A published temperature-dependent model of malaria transmission suitability was applied to continental gridded climate data for multiple future AR5 climate model projections. The resulting outcomes were aligned with programmatic needs to provide summaries at national and regional scales for the African continent. Model outcomes were combined with population projections to estimate the population at risk at three points in the future, 2030, 2050, and 2080, under two scenarios of greenhouse gas emissions (RCP4.5 and RCP8.5). RESULTS: Estimated geographic shifts in endemic and seasonal suitability for malaria transmission were observed across all future scenarios of climate change. The worst-case regional scenario (RCP8.5) of climate change predicted an additional 75.9 million people at risk from endemic (10-12 months) exposure to malaria transmission in Eastern and Southern Africa by the year 2080, with the greatest population at risk in Eastern Africa. Despite a predominance of reduction in season length, a net gain of 51.3 million additional people is predicted be put at some level of risk in Western Africa by midcentury. CONCLUSIONS: This study provides an updated view of potential malaria geographic shifts in Africa under climate change for the more recent climate model projections (AR5), and a tool for aligning findings with programmatic needs at key scales for decision-makers. In describing shifting seasonality, it was possible to capture transitions between endemic and epidemic risk areas, to facilitate the planning for interventions aimed at year-round risk versus anticipatory surveillance and rapid response to potential outbreak locations.
INTRODUCTION: Malaria is an infectious disease of high transmission in the Amazon region, but its dynamics and spatial distribution may vary depending on the interaction of environmental, socio-cultural, economic, political and health services factors. OBJECTIVE: To verify the existence of malaria case patterns in consonance with the fluviometric regimes in Amazon basin. METHOD: Methods of descriptive and inferential statistics were used in malaria and water level data for 35 municipalities in the Amazonas State, in the period from 2003 to 2014. RESULTS: The existence of a tendency to modulate the seasonality of malaria cases due to distinct periods of rivers flooding has been demonstrated. Differences were observed in the annual hydrological variability accompanied by different patterns of malaria cases, showing a trend of remodeling of the epidemiological profile as a function of the flood pulse. CONCLUSION: The study suggests the implementation of regional and local strategies considering the hydrological regimes of the Amazon basin, enabling municipal actions to attenuate the malaria in the Amazonas State.
BACKGROUND: Between 1999 and 2008 Russia experienced a flare-up of transmission of vivax malaria following its massive importation with more than 500 autochthonous cases in European Russia, the Moscow region being the most affected. The outbreak waned soon after a decrease in importation in mid-2000s and strengthening the control measures. Compared with other post-eradication epidemics in Europe this one was unprecedented by its extension and duration. METHODS: The aim of this study is to identify geographical determinants of transmission. The degree of favourability of climate for vivax malaria was assessed by measuring the sum of effective temperatures and duration of season of effective infectivity using data from 22 weather stations. For geospatial analysis, the locations of each of 405 autochthonous cases detected in Moscow region have been ascertained. A MaxEnt method was used for modelling the territorial differentiation of Moscow region according to the suitability of infection re-emergence based on the statistically valid relationships between the distribution of autochthonous cases and environmental and climatic factors. RESULTS: In 1999-2004, in the beginning of the outbreak, meteorological conditions were extremely favourable for malaria in 1999, 2001 and 2002, especially within the borders of the city of Moscow and its immediate surroundings. The greatest number of cases occurred at the northwestern periphery of the city and in the adjoining rural areas. A significant role was played by rural construction activities attracting migrant labour, vegetation density and landscape division. A cut-off altitude of 200 m was observed, though the factor of altitude did not play a significant role at lower altitudes. Most likely, the urban heat island additionally amplified malaria re-introduction. CONCLUSION: The malariogenic potential in relation to vivax malaria was high in Moscow region, albeit heterogeneous. It is in Moscow that the most favourable conditions exist for vivax malaria re-introduction in the case of a renewed importation. This recent event of large-scale re-introduction of vivax malaria in a temperate area can serve as a case study for further research.
The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.
Climate change is postulated to alter the distribution and abundance of species which serve as vectors for pathogens and is thus expected to affect the transmission of infectious, vector-borne diseases such as malaria. The ability to project and therefore, to mitigate the risk of potential expansion of infectious diseases requires an understanding of how vectors respond to environmental change. Here, we used an extensive dataset on the distribution of the mosquito Anopheles sacharovi, a vector of malaria parasites in Greece, southeast Europe, to build a modeling framework that allowed us to project the potential species range within the next decades. In order to account for model uncertainty, we employed a multi-model approach, combining an ensemble of diverse correlative niche models and a mechanistic model to project the potential expansion of species distribution and to delineate hotspots of potential malaria risk areas. The performance of the models was evaluated using official records on autochthonous malaria incidents. Our projections demonstrated a gradual increase in the potential range of the vector distribution and thus, in the malaria receptive areas over time. Linking the model outputs with human population inhabiting the study region, we found that population at risk increases, relative to the baseline period. The methodological framework proposed and applied here, offers a solid basis for a climate change impact assessment on malaria risk, facilitating informed decision making at national and regional scales.
Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012-2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.
Malaria is a Plasmodium parasitic disease transmitted by infected female Anopheles mosquitoes. Climatic factors, such as temperature, humidity, rainfall, and wind, have significant effects on the incidence of most vector-borne diseases, including malaria. The mosquito behavior, life cycle, and overall fitness are affected by these climatic factors. This paper presents the results obtained from investigating the optimal control strategies for malaria in the presence of temperature variation using a temperature-dependent malaria model. The study further identified the temperature ranges in four different geographical regions of sub-Saharan Africa, suitable for mosquitoes. The optimal control strategies in the temperature suitable ranges suggest, on average, a high usage of both larvicides and adulticides followed by a moderate usage of personal protection such as bednet. The average optimal bednet usage mimics the solution profile of the mosquitoes as the mosquitoes respond to changes in temperature. Following the results from the optimal control, this study also investigates using a temperature-dependent model with insecticide-sensitive and insecticide-resistant mosquitoes the impact of insecticide-resistant mosquitoes on disease burden when temperature varies. The results obtained indicate that optimal bednet usage on average is higher when insecticide-resistant mosquitoes are present. Besides, the average bednet usage increases as temperature increases to the optimal temperature suitable for mosquitoes, and it decreases after that, a pattern similar to earlier results involving insecticide-sensitive mosquitoes. Thus, personal protection, particularly the use of bednets, should be encouraged not only at low temperatures but particularly at high temperatures when individuals avoid the use of bednets. Furthermore, control and reduction of malaria may be possible even when mosquitoes develop resistance to insecticides.
Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R(2) (RsqB). Whereas, the predictive ability of the model is measured using a cross-validation technique. As a result, it is found that the square root of malaria cases with lag 1, maximum temperature, and relative humidity with lag 3 and 0 (respectively), are significantly positively associated with the square root of the cases. However, the minimum and average temperatures with lag 2, respectively, are observed as negatively (significantly) related. The considered model accounts for moderate amount of variation in the square root of malaria cases as received through the results for RsqB. We also present Absolute Percentage Errors (APE) for each of the 12 months (January-December) for a better understanding of the seasonal pattern of the predicted (square root of) malaria cases. Most of the APEs obtained corresponding to test data points is reasonably low. Further, the analysis shows that the considered model closely predicted the actual (square root of) malaria cases, except for some peak cases during the particular months. The output of the current research might help the district to develop and strengthen early warning prediction of malaria cases for proper mitigation, eradication, and prevention in similar settings.
It is often difficult to define the relationship and the influence of climate on the occurrence and distribution of disease. To examine this issue, the effects of climate indices on the distributions of malaria and meningitis in Nigeria were assessed over space and time. The main purpose of the study was to evaluate the relationships between climatic variables and the prevalence of malaria and meningitis, and develop an early warning system for predicting the prevalence of malaria and meningitis as the climate varies. An early warning system was developed to predetermine the months in a year that people are vulnerable to malaria and meningitis. The results revealed a significant positive relationship between rainfall and malaria, especially during the wet season with correlation coefficient R-2 >= 60.0 in almost all the ecological zones. In the Sahel, Sudan and Guinea, there appears to be a strong relationship between temperature and meningitis with R-2 > 60.0. In all, the results further reveal that temperatures and aerosols have a strong relationship with meningitis. The assessment of these initial data seems to support the finding that the occurrence of meningitis is higher in the northern region, especially the Sahel and Sudan. In contrast, malaria occurrence is higher in the southern part of the study area. We suggest that a thorough investigation of climate parameters is critical for the reallocation of clinical resources and infrastructures in economically underprivileged regions.
BACKGROUND AND OBJECTIVE: Malaria is an arthropod-borne infectious disease transmitted by the mosquito Anopheles and claims millions of lives globally every year. Reasons for failure to eradicate this disease are multifactorial. The seasonality of the malaria is principally determined by climatic factors conducive for breeding of the vector. We aimed to study the relationship between climatic variability and the seasonality of malaria over an eight-year duration. METHODS: This was a retrospective medical chart review of 8,844 confirmed cases of malaria which presented to The Indus Hospital, Karachi from January 2008 to November 2015. Cases were plotted against meteorological data for Karachi to elicit monthly variation. RESULTS: A secular incline and seasonality in malaria cases over the duration of eight years was seen. More cases were reported in the summer, rainy season compared with the other three seasons in each year. There was significant association with specific climate variables such as temperature, moisture, and humidity. CONCLUSION: There is a marked seasonal variation of malaria in Karachi, influenced by various environmental factors. Identification of the ‘the concentrated period’ of malaria can be helpful for policymakers to deploy malaria control interventions.
BACKGROUND: Malaria remains a major tropical vector-borne disease of immense public health concern owing to its debilitating effects in sub-Saharan Africa. Over the past 30?years, the high altitude areas in Eastern Africa have been reported to experience increased cases of malaria. Governments including that of the Republic of Uganda have responded through intensifying programs that can potentially minimize malaria transmission while reducing associated fatalities. However, malaria patterns following these intensified control and prevention interventions in the changing climate remains widely unexplored in East African highland regions. This study thus analyzed malaria patterns across altitudinal zones of Mount Elgon, Uganda. METHODS: Times-series data on malaria cases (2011-2017) from five level III local health centers occurring across three altitudinal zones; low, mid and high altitude was utilized. Inverse Distance Weighted (IDW) interpolation regression and Mann Kendall trend test were used to analyze malaria patterns. Vegetation attributes from the three altitudinal zones were analyzed using Normalized Difference Vegetation Index (NDVI) was used to determine the Autoregressive Integrated Moving Average (ARIMA) model was used to project malaria patterns for a 7 year period. RESULTS: Malaria across the three zones declined over the study period. The hotspots for malaria were highly variable over time in all the three zones. Rainfall played a significant role in influencing malaria burdens across the three zones. Vegetation had a significant influence on malaria in the higher altitudes. Meanwhile, in the lower altitude, human population had a significant positive correlation with malaria cases. CONCLUSIONS: Despite observed decline in malaria cases across the three altitudinal zones, the high altitude zone became a malaria hotspot as cases variably occurred in the zone. Rainfall played the biggest role in malaria trends. Human population appeared to influence malaria incidences in the low altitude areas partly due to population concentration in this zone. Malaria control interventions ought to be strengthened and strategically designed to achieve no malaria cases across all the altitudinal zones. Integration of climate information within malaria interventions can also strengthen eradication strategies of malaria in such differentiated altitudinal zones.
Kerteszia cruzii is a sylvatic mosquito and the primary vector of Plasmodium spp., which can cause malaria in humans in areas outside the Amazon River basin in Brazil. Anthropic changes in the natural environments are the major drivers of massive deforestation and local climate change, with serious impacts on the dynamics of mosquito communities and on the risk of acquiring malaria. Considering the lack of information on the dynamics of malaria transmission in areas across the Atlantic Forest biome, where Ke. cruzii is the dominant vector, and the impact of climate drivers of malaria, the present study aimed to: (i) investigate the occurrence and survival rate of Ke. cruzii based on the distinct vegetation profiles found in areas across the coastal region of the Brazilian Atlantic Forest biome; (ii) estimate the extrinsic incubation period (EIP) and survival rates of P. vivax and P. falciparum parasites in Ke. cruzii under current and future scenarios. The potential distribution of Plasmodium spp. was estimated using simulation analyses under distinct scenarios of average temperature increases from 1 °C to 3.7 °C. Our results showed that two conditions are necessary to explain the occurrence and survival of Ke. cruzii: warm temperature and presence of the Atlantic Forest biome. Moreover, both Plasmodium species showed a tendency to decrease their EIP and increase their estimated survival rates in a scenario of higher temperature. Our findings support that the high-risk malaria areas may include the southern region of the distribution range of the Atlantic Forest biome in the coming years. Despite its limitations and assumptions, the present study provides robust evidence of areas with potential to be impacted by malaria incidence in a future scenario. These areas should be monitored in the next decades regarding the occurrence of the mosquito vector and the potential for malaria persistence and increased occurrence.
Continental-scale models of malaria climate suitability typically couple well-established temperature-response models with basic estimates of vector habitat availability using rainfall as a proxy. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to the precipitation threshold than the thermal response curve applied. To address this problem we use downscaled daily climate predictions from seven GCMs to run a continental-scale hydrological model for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The estimated hydro-climatically suitable area for stable malaria transmission is smaller than previous models suggest and shows only a very small increase in state-of-the-art future climate scenarios. However, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when using a hydrological model to estimate surface water availability for vector breeding.
Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 C and 2.0 C global warming levels (hereinafter GWL1.5 and GWL2.0, respectively). Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios. Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 C and 1.5 C and 1.0 C to 2.0 C under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region. Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.
BACKGROUND: Malaria is a mosquito-borne infectious disease known to cause significant numbers of morbidities and mortalities across the globe. In Ethiopia, its transmission is generally seasonal and highly unstable due to variations in topography and rainfall patterns. Studying the trends in malaria in different setups is crucial for area-specific evidence-based interventions, informed decisions, and to track the effectiveness of malaria control programs. The trend in malaria infections in the area has not been documented. Hence, this study aimed to assess the five-year trend in microscopically confirmed malaria cases in Dembecha Health Center, West Gojjam Zone, Amhara national regional state, Ethiopia. METHODS: A health facility-based retrospective study was conducted in Dembecha Health Center from February to April 2018. All microscopically confirmed malaria cases registered between 2011/12 and 2015/16 were carefully reviewed from laboratory record books and analyzed accordingly. RESULTS: A total of 12,766 blood films were requested over the last five years at Dembecha Health Center. The number of microscopically confirmed malaria cases was 2086 (16.34%). The result showed a fluctuating yet declining trend in malaria infections. The highest number of cases was registered in 2012/13, while the lowest was in 2015/16. Males and age groups >20 constituted 58.9% and 44.2% of the patients, respectively, being the hardest hit by malaria in the area. Malaria existed in almost every month and seasons. Plasmodium falciparum was the predominant species. The highest peak of malaria infections was observed in the late transition (October-December) 799 (38.3%) and early transition (May-June) 589 (28.2%) seasons. CONCLUSION: Although the results indicate a fluctuating yet declining trend, the prevalence of confirmed malaria cases in the area remains alarming and indicates a major public health burden. Therefore, close monitoring and intervention measures to control malaria infections in the area and also to tackle the dominant species, Plasmodium falciparum, are necessitated accordingly.
Although only a small proportion of the landmass of South Africa is classified as high risk for malaria, the country experiences on-going challenges relating to malaria outbreaks. Climate change poses a growing threat to this already dire situation. While considerable effort has been placed in public health campaigns in the highest-risk regions, and national malaria maps are updated to account for changing climate, malaria cases have increased. This pilot study considers the sub-population of South Africans who reside outside of the malaria area, yet have the means to travel into this high-risk region for vacation. Through the lens of the governmental “ABC of malaria prevention”, we explore this sub-population’s awareness of the current boundaries to the malaria area, perceptions of the future boundary under climate change, and their risk-taking behaviours relating to malaria transmission. Findings reveal that although respondents self-report a high level of awareness regarding malaria, and their boundary maps reveal the broad pattern of risk distribution, their specifics on details are lacking. This includes over-estimating both the current and future boundaries, beyond the realms of climate-topographic possibility. Despite over-estimating the region of malaria risk, the respondents reveal an alarming lack of caution when travelling to malaria areas. Despite being indicated for high-risk malaria areas, the majority of respondents did not use chemoprophylaxis, and many relied on far less-effective measures. This may in part be due to respondents relying on information from friends and family, rather than medical or governmental advice.
Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1-7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8-7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7-27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9-25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.
BACKGROUND: The World Health Organization (WHO) promotes long-lasting insecticidal nets (LLIN) and indoor residual house-spraying (IRS) for malaria control in endemic countries. However, long-term impact data of vector control interventions is rarely measured empirically. METHODS: Surveillance data was collected from paediatric admissions at Tororo district hospital for the period January 2012 to December 2019, during which LLIN and IRS campaigns were implemented in the district. Malaria test positivity rate (TPR) among febrile admissions aged 1 month to 14 years was aggregated at baseline and three intervention periods (first LLIN campaign; Bendiocarb IRS; and Actellic IRS?+?second LLIN campaign) and compared using before-and-after analysis. Interrupted time-series analysis (ITSA) was used to determine the effect of IRS (Bendiocarb?+?Actellic) with the second LLIN campaign on monthly TPR compared to the combined baseline and first LLIN campaign periods controlling for age, rainfall, type of malaria test performed. The mean and median ages were examined between intervention intervals and as trend since January 2012. RESULTS: Among 28,049 febrile admissions between January 2012 and December 2019, TPR decreased from 60% at baseline (January 2012-October 2013) to 31% during the final period of Actellic IRS and LLIN (June 2016-December 2019). Comparing intervention intervals to the baseline TPR (60.3%), TPR was higher during the first LLIN period (67.3%, difference 7.0%; 95% CI 5.2%, 8.8%, p?<?0.001), and lower during the Bendiocarb IRS (43.5%, difference -?16.8%; 95% CI -?18.7%, -?14.9%) and Actellic IRS (31.3%, difference -?29.0%; 95% CI -?30.3%, -?27.6%, p?<?0.001) periods. ITSA confirmed a significant decrease in the level and trend of TPR during the IRS (Bendicarb?+?Actellic) with the second LLIN period compared to the pre-IRS (baseline?+?first LLIN) period. The age of children with positive test results significantly increased with time from a mean of 24 months at baseline to 39 months during the final IRS and LLIN period. CONCLUSION: IRS can have a dramatic impact on hospital paediatric admissions harbouring malaria infection. The sustained expansion of effective vector control leads to an increase in the age of malaria positive febrile paediatric admissions. However, despite large reductions, malaria test-positive admissions continued to be concentrated in children aged under five years. Despite high coverage of IRS and LLIN, these vector control measures failed to interrupt transmission in Tororo district. Using simple, cost-effective hospital surveillance, it is possible to monitor the public health impacts of IRS in combination with LLIN.
