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.
