Dengue is now a major health concern in sub-Saharan Africa. Understanding the influence of local meteorological factors on the incidence of dengue is an important element for better prediction and control of this disease. This study aims to assess the impact of meteorological factors on dengue transmission in the central region of Burkina Faso. We analyzed the lagged effects of meteorological factors on the weekly incidence of dengue from 2017 to 2019 in the central region of Burkina Faso using a General Additive Model. The results show that maximum and minimum temperature, relative humidity, and wind speed have a significant non-linear effect on dengue cases in the region with 83% of case variance explained. The optimal temperature that increases dengue cases was 27°C to 32°C for the maximum temperature and 18°C to 20°C for the minimum temperature with a decrease beyond that. The maximum temperature shifted by six weeks had the best correlation with dengue incidence. The estimated number of dengue cases increases as the maximum relative humidity increases from 15 to 45% and then from 60 to 70%. In general, an increase in daily wind speed is estimated to decrease the number of daily dengue cases. The relationship between rainfall and dengue cases was not significant. This study provides local information about the effect of meteorological factors on dengue that should help improve predictive models of dengue cases in Burkina Faso and contribute to the control of this disease.
Dengue Fever (DF) is an important arthropod-borne viral infection that has repeatedly occurred as outbreaks in eastern and northeastern Ethiopia since 2013. A cross-sectional epidemiological outbreak investigation was carried out from September to November 2019 on febrile patients (confirmed malaria negative) who presented with suspected and confirmed DF at both public and private health facilities in Gewane District, Afar Region, northeastern Ethiopia. Entomological investigation of containers found in randomly selected houses belonging to DF-positive patients was undertaken to survey for the presence of Aedes larvae/pupae. A total of 1185 DF cases were recorded from six health facilities during the 3-month study period. The mean age of DF cases was 27.2 years, and 42.7% of cases were female. The most affected age group was 15−49 years old (78.98%). The total case proportions differed significantly across age groups when compared to the population distribution; there were approximately 15% and 5% higher case proportions among those aged 15−49 years and 49+ years, respectively. A total of 162 artificial containers were inspected from 62 houses, with 49.4% found positive for Aedes aegypti larva/pupae. Aedes mosquitoes were most commonly observed breeding in plastic tanks, tires, and plastic or metal buckets/bowls. World Health Organization entomological indices classified the study site as high risk for dengue virus outbreaks (House Index = 45.2%, Container Index = 49.4%, and Breteau Index = 129). Time series climate data, specifically rainfall, were found to be significantly predictive of AR (p = 0.035). Study findings highlight the importance of vector control to prevent future DF outbreaks in the region. The scarcity of drinking water and microclimatic conditions may have also contributed to the occurrence of this outbreak.
Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4-30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.
Dengue contributes a significant burden on global public health and economies. In Africa, the burden of dengue virus (DENV) infection is not well described. This review was undertaken to determine the prevalence of dengue and associated risk factors. A literature search was done on PubMed/MEDLINE, Scopus, Embase, and Google Scholar databases to identify articles published between 1960 and 2020. Meta-analysis was performed using a random-effect model at a 95% confidence interval, followed by subgroup meta-analysis to determine the overall prevalence. Between 1960 and 2020, 45 outbreaks were identified, of which 17 and 16 occurred in East and West Africa, respectively. Dengue virus serotype 1 (DENV-1) and DENV-2 were the dominant serotypes contributing to 60% of the epidemics. Of 2211 cases reported between 2009 and 2020; 1954 (88.4%) were reported during outbreaks. Overall, the prevalence of dengue was 29% (95% CI: 20-39%) and 3% (95% CI: 1-5%) during the outbreak and non-outbreak periods, respectively. Old age (6/21 studies), lack of mosquito control (6/21), urban residence (4/21), climate change (3/21), and recent history of travel (3/21) were the leading risk factors. This review reports a high burden of dengue and increased risk of severe disease in Africa. Our findings provide useful information for clinical practice and health policy decisions to implement effective interventions.
Background The world has been battling several vector-borne diseases since time immemorial. Socio-economic marginality, precipitation variations and human behavioral attributes play a major role in the proliferation of these diseases. Lockdown and social distancing have affected social behavioral aspects of human life and somehow impact on the spread of vector borne diseases. This article sheds light into the relationship between COVID-19 lockdown and global dengue burden with special focus on India. It also focuses on the interconnection of the COVID-19 pandemic (waves 1 and 2) and the alteration of human behavioral patterns in dengue cases. Methods We performed a systematic search using various resources from different platforms and websites, such as Medline; Pubmed; PAHO; WHO; CDC; ECDC; Epidemiology Unit Ministry of Health (Sri Lanka Government); NASA; NVBDCP from 2015 until 2021. We have included many factors, such as different geographical conditions (tropical climate, semitropic and arid conditions); GDP rate (developed nations, developing nations, and underdeveloped nations). We also categorized our data in order to conform to COVID-19 duration from 2019 to 2021. Data was extracted for the complete duration of 10 years (2012 to 2021) from various countries with different geographical region (arid region, semitropic/semiarid region and tropical region). Results There was a noticeable reduction in dengue cases in underdeveloped (70-85%), developing (50-90%), and developed nations (75%) in the years 2019 and 2021. The dengue cases drastically reduced by 55-65% with the advent of COVID-19 s wave in the year 2021 across the globe. Conclusions At present, we can conclude that COVID-19 and dengue show an inverse relationship. These preliminary, data-based observations should guide clinical practice until more data are made public and basis for further medical research.
Vector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host-vector interactions is expressed as vectorial capacity-a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.
Introduction: Dengue is a mosquito borne viral disease. found in tropical and subtropical countries. Dengue virus (DENV) infected mosquitoes of Aedes species are crucial for the transmission of disease. It has emerged as a threat to the public health systems. Dengue is endemic in many parts of India but still the status of dengue cases in Rewa Madhya Pradesh is not reported convincingly. Aim: To investigate the presence of dengue in Rewa district of Madhya Pradesh. Materials and Methods: This cross-sectional study was conducted in the Department of Microbiology at Shyam Shah Medical college Rewa under National Vector Borne Disease Control Programme (NVBDCP), Rewa, Madhya Pradesh, India, including 1113 Outpatient/Inpatient Department samples received during March 2021 to October 2021. Blood samples were collected from patients having febrile illness and after serum separation, serum were subjected to NS1 Enzyme Linked Immunosorbent Assay (ELISA) test. Descriptive statistics and Chi-square tests were applied for data analysis. Results: A total of 1113 sample were received and tested for dengue NS1 out of that 108 sample were found NS1 positive by ELISA. The cases of dengue started from the month of July 2021. But in the month of October dengue positivity was highest in number. Dengue cases reported were 297 (6.73%) in the rainy season (July-August), but the dengue positivity increased (713, 9.3%) in the post rainy season (September-October). Overall prevalence of dengue was higher in the 21-30 years (34.3%) age group followed by 11-20 years (24.1%), 31-40 years (18.5%), 41-50 years (18.5%), 51-60 years (7.4%) and >60 years (3.70%) age groups with respect to total positive cases. The prevalence of dengue was higher in male (12.94%) in comparison to females (5.54%). Conclusion: This study warrants the dengue virus infection as one of the important causes of fever during rainy and post rainy season in this region. Early diagnosis and reporting of cases are important for the better management of disease.
In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.
INTRODUCTION: The study aimed to develop a reproducible, open-source, and scalable framework for extracting climate data from satellite imagery, understanding dengue’s decadal trend in India, and estimating the relationship between dengue occurrence and climatic factors. MATERIALS AND METHODS: A framework was developed in the Open Source Software, and it was empirically tested using reported annual dengue occurrence data in India during 2010-2019. Census 2011 and population projections were used to calculate incidence rates. Zonal statistics were performed to extract climate parameters. Correlation coefficients were calculated to estimate the relationship of dengue with the annual average of daily mean and minimum temperature and rainy days. RESULTS: Total 818,973 dengue cases were reported from India, with median annual incidence of 6.57 per lakh population; it was high in 2019 and 2017 (11.80 and 11.55 per lakh) and the Southern region (8.18 per lakh). The highest median annual dengue incidence was observed in Punjab (24.49 per lakh). Daily climatic data were extracted from 1164 coordinate locations across the country for the decadal period (4,249,734 observations). The annual average of daily temperature and rainy days positively correlated with dengue in India (r = 0.31 and 0.06, at P < 0.01 and 0.30, respectively). CONCLUSION: The study provides a reproducible algorithm for bulk climatic data extraction from research-level satellite imagery. Infectious disease models can be used to understand disease epidemiology and strengthen disease surveillance in the country.
India has witnessed a five-fold increase in dengue incidence in the past decade. However, the nation-wide distribution of dengue vectors, and the impacts of climate change are not known. In this study, species distribution modeling was used to predict the baseline and future distribution of Aedine vectors in India on the basis of biologically relevant climatic indicators. Known occurrences of Aedes aegypti and Aedes albopictus were obtained from the Global Biodiversity Information Facility database and previous literature. Bio-climatic variables were used as the potential predictors of vector distribution. After eliminating collinear and low contributing predictors, the baseline and future prevalence of Aedes aegypti and Aedes albopictus was determined, under three Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), using the MaxEnt species distribution model. Aedes aegypti was found prevalent in most parts of the southern peninsula, the eastern coastline, north eastern states and the northern plains. In contrast, Aedes albopictus has localized distribution along the eastern and western coastlines, north eastern states and in the lower Himalayas. Under future scenarios of climate change, Aedes aegypti is projected to expand into unsuitable regions of the Thar desert, whereas Aedes albopictus is projected to expand to the upper and trans Himalaya regions of the north. Overall, the results provide a reliable assessment of vectors prevalence in most parts of the country that can be used to guide surveillance efforts, despite minor disagreements with dengue incidence in Rajasthan and the north east, possibly due to behavioral practices and sampling efforts. Plain Language Summary Climatic parameters derived from temperature and humidity affect the development and survival of mosquitoes that spread diseases. In the past decade, India has witnessed an alarming rise in dengue, a viral disease that spreads through the bite of the mosquitoes Aedes aegypti and Aedes albopictus. We used machine learning based modeling algorithm to predict the present and future abundance of these mosquitoes in India, based on biologically relevant climatic factors. The results project expansion of Aedes aegypti in the hot arid regions of the Thar Desert and Aedes albopictus in cold upper Himalayas as a result of future climatic changes. The results provide a useful guide for strengthening efforts for entomological and dengue surveillance.
Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall’s tau_b, Spearman’s rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.
Dengue is endemic in Bangladesh and is an important cause of morbidity and mortality. Suppressing the mosquito vector activity at the optimal time annually is a practical strategy to control dengue outbreaks. The objective of this study was to estimate the monthly growth factor (GF) of dengue cases over the past 12 years as a means to identify the optimal time for a vector-control programme in Bangladesh. We reviewed the monthly cases reported by the Institute of Epidemiology, Disease Control and Research of Bangladesh during the period of January 2008-December 2019. We calculated the GF of dengue cases between successive months during this period and report means and 95% confidence intervals (CI). The median number of patients admitted to the hospital with dengue fever per year was 1554 (range: 375-101,354). The mean monthly GF of dengue cases was 1.2 (95% CI: 0.4-2.4). The monthly GF lower CI between April and July was > 1, whereas from September to November and January the upper CI was <1. The highest GF of dengue was recorded in June (mean: 2.4; 95% CI: 1.7-3.5) and lowest in October (mean: 0.43; 95% CI: 0.24-0.73). More than 81% (39/48) months between April and July for the period 2008-2019 had monthly GF >1 compared to 20% (19/96) months between August and March of the same period. The monthly GF was significantly correlated with monthly rainfall (r = 0.39) and monthly mean temperature (r = 0.30). The growth factor of the dengue cases over the last 12 years appeared to follow a marked periodicity linked to regional rainfall patterns. The increased transmission rate during the months of April-July, a seasonally determined peak suggests the need for strengthening a range of public health interventions, including targeted vector control efforts and community education campaigns.
Numerous studies on climate change and variability have revealed that these phenomena have noticeable influence on the epidemiology of dengue fever, and such relationships are complex due to the role of the vector—the Aedes mosquitoes. By undertaking a step-by-step approach, the present study examined the effects of climatic factors on vector abundance and subsequent effects on dengue cases of Dhaka city, Bangladesh. Here, we first analyzed the time-series of Stegomyia indices for Aedes mosquitoes in relation to temperature, rainfall and relative humidity for 2002–2013, and then in relation to reported dengue cases in Dhaka. These data were analyzed at three sequential stages using the generalized linear model (GLM) and generalized additive model (GAM). Results revealed strong evidence that an increase in Aedes abundance is associated with the rise in temperature, relative humidity, and rainfall during the monsoon months, that turns into subsequent increases in dengue incidence. Further we found that (i) the mean rainfall and the lag mean rainfall were significantly related to Container Index, and (ii) the Breteau Index was significantly related to the mean relative humidity and mean rainfall. The relationships of dengue cases with Stegomyia indices and with the mean relative humidity, and the lag mean rainfall were highly significant. In examining longitudinal (2001–2013) data, we found significant evidence of time lag between mean rainfall and dengue cases.
BACKGROUND: Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000-2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world’s most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study. METHODS: We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000-2020. RESULTS: We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks. CONCLUSIONS: The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.
Aedes aegypti mosquitoes are the main vector of dengue viruses globally and are present throughout much of the state of Florida (FL) in the United States of America. However, local transmission of dengue viruses in FL has mainly occurred in the southernmost counties; specifically Monroe and Miami-Dade counties. To get a better understanding of the ecologic risk factors for dengue fever incidence throughout FL, we collected and analyzed numerous environmental factors that have previously been connected to local dengue cases in disease-endemic regions. We analyzed these factors for each county-year in FL, between 2009-2019, using negative binomial regression. Monthly minimum temperature of 17.5-20.8 °C, an average temperature of 26.1-26.7 °C, a maximum temperature of 33.6-34.7 °C, rainfall between 11.4-12.7 cm, and increasing numbers of imported dengue cases were associated with the highest risk of dengue incidence per county-year. To our knowledge, we have developed the first predictive model for dengue fever incidence in FL counties and our findings provide critical information about weather conditions that could increase the risk for dengue outbreaks as well as the important contribution of imported dengue cases to local establishment of the virus in Ae. aegypti populations.
Dengue is one of the major health problems in the state of Chiapas. Consequently, spatial information on the distribution of the disease can optimize directed control strategies. Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. This is an ecological study that uses data from a range of sources. Dengue cases occurred from January to August 2019. The data analysis used the spatial correlation of dengue cases (DCs), which was calculated with the Moran index statistic, and a generalized linear spatial model (GLSM) within a Bayesian framework, which was considered to model the spatial distribution of DCs in the state of Chiapas. We selected the climatological, geographic, and sociodemographic variables related to the study area. A prediction of the model on Chiapas maps was carried out based on the places where the cases were registered. We find a spatial correlation of 0.115 (p value=0.001)between neighboring municipalities using the Moran index. The variables that have an effect on the number of confirmed cases of dengue are the maximum temperature (Coef=0.110; 95% CrI: 0.076 – 0.215), rainfall (Coef=0.013; 95% CrI:0.008 – 0.028), and altitude (Coef=0.00045; 95% CrI:0.00002 – 0.00174) of each municipality. The predicting power is notably better in regions that have a greater number of municipalities where DCs are registered. The model shows the importance of considering these variables to prevent future DCs in vulnerable areas.
OBJECTIVES: To study the epidemiology of dengue incidence and understand the dynamics of dengue transmission in Makkah, Kingdom of Saudi Arabia (KSA), between 2017-2019. METHODS: This is a cross-sectional study. Health and demographic data was obtained for all confirmed dengue cases in Makkah, KSA, in the years 2017-2019 from the Vector-Borne and Zoonotic Diseases Administration (VBZDA) in Makkah and the Makkah Regional Laboratory, KSA. In addition, entomological data about Aedes density was obtained from the VBZDA. Descriptive epidemiological methods were used to determine the occurrence and distribution of dengue cases. RESULTS: Laboratory-confirmed dengue cases were higher in 2019 as compared to 2017 and 2018, suggesting an outbreak of dengue in Makkah, KSA, in 2019. The incidence of confirmed dengue cases was 204 in 2017, 163 in 2018 and 748 in 2019. Dengue mostly affected people in the 25-44 age group, accounting for approximately half of the annual dengue cases each year. Men were at a higher dengue incidence risk when compared to women, and Saudi women had a higher risk rate for dengue cases when compared to non-Saudi women in all 3 years studied. There was no dengue related death in these 3 years. CONCLUSION: The dengue incidence increased in Makkah, KSA, in 2019 as compared to the previous 2 years, owing to heavy rainfall in 2019. Post-rainfall Vector control efforts may help contain the disease in Makkah, KSA.
OBJECTIVE: Increased temperature and humidity across the world and emergence of mosquito-borne diseases, notably dengue both continue to present public health problems, but their relationship is not clear as conflicting evidence abound on the association between climate conditions and risk of dengue fever. This characterization is important as mitigation of climate change-related variables will contribute toward efficient planning of health services. The purpose of this study was to determine whether humidity in addition to high temperatures increase the risk of dengue transmission. METHODS: We have assessed the joint association between temperature and humidity with the incidence of dengue fever at Jeddah City in Saudi Arabia. We obtained weekly data from Jeddah City on temperature and humidity between 2006 and 2009 for 200 weeks starting week 1/2006 and ending week 53/2009. We also collected incident case data on dengue fever in Jeddah City. RESULTS: The cross-tabulated analysis showed an association between temperature or humidity conditions and incident cases of dengue. Our data found that hot and dry conditions were associated with a high risk of dengue incidence in Jeddah City. CONCLUSION: Hot and dry conditions are risk factors for dengue fever.
The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999-2014 was used for training and the three subsequent years of incidence 2015-2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.
Dengue fever has been endemic in Paraguay since 2009 and is a major cause of public-health-management-related burdens. However, Paraguay still lacks information on the association between climate factors and dengue fever. We aimed to investigate the association between climatic factors and dengue fever in Asuncion. Cumulative dengue cases from January 2014 to December 2020 were extracted weekly, and new cases and incidence rates of dengue fever were calculated. Climate factor data were aggregated weekly, associations between dengue cases and climate factors were analyzed, and variables were selected to construct our model. A generalized additive model was used, and the best model was selected based on Akaike information criteria. Piecewise regression analyses were performed for non-linear climate factors. Wind and relative humidity were negatively associated with dengue cases, and minimum temperature was positively associated with dengue cases when the temperature was less than 21.3 °C and negatively associated with dengue when greater than 21.3 °C. Additional studies on dengue fever in Asuncion and other cities are needed to better understand dengue fever.
Climate is considered an important factor in the temporal and spatial distribution of vector-borne diseases. Dengue transmission involves many factors: although it is not yet fully understood, climate is a critical factor as it facilitates risk analysis of epidemics. This study analyzed the effect of seasonal factors and the relationship between climate variables and dengue risk in the municipality of Campo Grande, from 2008 to 2018. Generalized linear models with negative binomial and Poisson distribution were used. The most appropriate model was the one with “minimum temperature” and “precipitation”, both lagged by one month, controlled by “year”. In this model, a 1 degrees C rise in the minimum temperature of one month led to an increase in dengue cases the following month, while a 10 mm increase in precipitation led to an increase in dengue cases the following month.
Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.
The way newspapers frame infectious disease outbreaks and their connection to the environmental determinants of disease transmission matter because they shape how we understand and respond to these major events. In 2017, following an unexpected climatic event named “El Niño Costero,” a dengue epidemic in Peru affected over seventy-five thousand people. This paper examines how the Peruvian news media presented dengue, a climate-sensitive disease, as a public health problem by analyzing a sample of 265 news stories on dengue from two major newspapers published between January 1st and December 31st of 2017. In analyzing the construction of responsibility for the epidemic, I find frames that blamed El Niño Costero’s flooding and Peru’s poorly prepared cities and public health infrastructure as the causes of the dengue outbreak. However, when analyzing frames that offer solutions to the epidemic, I find that news articles call for government-led, short-term interventions (e.g., fogging) that fail to address the decaying public health infrastructure and lack of climate-resilient health systems. Overall, news media tended to over-emphasize dengue as requiring technical solutions that ignore the root causes of health inequality and environmental injustice that allow dengue to spread in the first place. This case speaks to the medicalization of public health and to a long history of disease-control programs in the Global South that prioritized top-down technical approaches, turning attention away from the social and environmental determinants of health, which are particularly important in an era of climate change.
BACKGROUND: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. METHODS: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. FINDINGS: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0-3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41-1·73]) and 3-5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22-1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32-2·37] at 0 months lag vs maximum RR 1·58 [1·39-1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33-1·92] vs 1·15 [1·08-1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages. INTERPRETATION: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods. FUNDING: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.
In the last two decades dengue cases increased significantly throughout the world, giving place to more frequent outbreaks in Latin America. In the non-endemic city of San Ramón de la Nueva Orán, located in Northwest Argentina, large dengue outbreaks alternate with several years of smaller ones. This pattern, as well as the understanding of the underlying mechanisms, could be essential to design proper strategies to reduce epidemic size. We develop a stochastic model that includes climate variables, social structure, and mobility between a non-endemic city and an endemic area. Climatic variables were input of a mosquito population ecological model, which in turn was coupled to a meta-population, spatially explicit, epidemiological model. Human mobility was included into the model given the high border crossing to the northern country of Bolivia, where dengue transmission is sustained during the whole year. We tested different hypotheses regarding people mobility as well as climate variability by fitting numerical simulations to weekly clinical data reported from 2009 to 2016. After assessing the number of imported cases that triggered the observed outbreaks, our model allows to explain the observed epidemic pattern. We found that the number of vectors per host and the effective reproductive number are proxies for large epidemics. Both proxies are related with climate variability such as rainfall and temperature, opening the possibility to test these meteorological variables for forecast purposes.
Dengue is a re-emerging disease, currently considered the most important mosquito-borne arbovirus infection affecting humankind, taking into account both its morbidity and mortality. Brazil is considered an endemic country for dengue, such that more than 1,544,987 confirmed cases were notified in 2019, which means an incidence rate of 735 for every 100 thousand inhabitants. Climate is an important factor in the temporal and spatial distribution of vector-borne diseases, such as dengue. Thus, rainfall and temperature are considered macro-factors determinants for dengue, since they directly influence the population density of Aedes aegypti, which is subject to seasonal fluctuations, mainly due to these variables. This study examined the incidence of dengue fever related to the climate influence by using temperature and rainfall variables data obtained from remote sensing via artificial satellites in the metropolitan region of Rio de Janeiro, Brazil. The mathematical model that best fits the data is based on an auto-regressive moving average with exogenous inputs (ARMAX). It reproduced the values of incidence rates in the study period and managed to predict with good precision in a one-year horizon. The approach described in present work may be replicated in cities around the world by the public health managers, to build auxiliary operational tools for control and prevention tasks of dengue, as well of other arbovirus diseases.
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (R(sum)), mean temperature (T(mean)), mean relative humidity (RH(mean)), and mean normalized difference vegetation index (NDVI(mean)). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.
Dengue fever is re-emerging worldwide, however the reasons of this new emergence are not fully understood. Our goal was to report the incidence of dengue in one of the most populous States of Brazil, and to assess the high-risk areas using a spatial and spatio-temporal annual models including geoclimatic, demographic and socioeconomic characteristics. An ecological study with both, a spatial and a temporal component was carried out in Sao Paulo State, Southeastern Brazil, between January 1st, 2007 and December 31st, 2019. Crude and Bayesian empirical rates of dengue cases following by Standardized Incidence Ratios (SIR) were calculated considering the municipalities as the analytical units and using the Integrated Nested Laplace Approximation in a Bayesian context. A total of 2,027,142 cases of dengue were reported during the studied period. The spatial model allocated the municipalities in four groups according to the SIR values: (I) SIR<0.8; (II) SIR 0.8<1.2; (III) SIR 1.2<2.0 and SIR>2.0 identified the municipalities with higher risk for dengue outbreaks. “Hot spots” are shown in the thematic maps. Significant correlations between SIR and two climate variables, two demographic variables and one socioeconomical variable were found. No significant correlations were found in the spatio-temporal model. The incidence of dengue exhibited an inconstant and unpredictable variation every year. The highest rates of dengue are concentrated in geographical clusters with lower surface pressure, rainfall and altitude, but also in municipalities with higher degree of urbanization and better socioeconomic conditions. Nevertheless, annual consolidated variations in climatic features do not influence in the epidemic yearly pattern of dengue in southeastern Brazil.
BACKGROUND: This research addresses two questions: (1) how El Niño Southern Oscillation (ENSO) affects climate variability and how it influences dengue transmission in the Metropolitan Region of Recife (MRR), and (2) whether the epidemic in MRR municipalities has any connection and synchronicity. METHODS: Wavelet analysis and cross-correlation were applied to characterize seasonality, multiyear cycles, and relative delays between the series. This study was developed into two distinct periods. Initially, we performed periodic dengue incidence and intercity epidemic synchronism analyses from 2001 to 2017. We then defined the period from 2001 to 2016 to analyze the periodicity of climatic variables and their coherence with dengue incidence. RESULTS: Our results showed systematic cycles of 3-4 years with a recent shortening trend of 2-3 years. Climatic variability, such as positive anomalous temperatures and reduced rainfall due to changes in sea surface temperature (SST), is partially linked to the changing epidemiology of the disease, as this condition provides suitable environments for the Aedes aegypti lifecycle. CONCLUSION: ENSO may have influenced the dengue temporal patterns in the MRR, transiently reducing its main way of multiyear variability (3-4 years) to 2-3 years. Furthermore, when the epidemic coincided with El Niño years, it spread regionally and was highly synchronized.
Tropical countries face urgent public health challenges regarding epidemic control of Dengue. Since effective vector-control efforts depend on the timing in which public policies take place, there is an enormous demand for accurate prediction tools. In this work, we improve upon a recent approach of coarsely predicting outbreaks in Brazilian urban centers based solely on their yearly climate data. Our methodological advancements encompass a judicious choice of data pre-processing steps and usage of modern computational techniques from signal-processing and manifold learning. Altogether, our results improved earlier prediction accuracy scores from 0.72 to 0.80, solidifying manifold learning on climate data alone as a viable way to make (coarse) dengue outbreak prediction in large urban centers. Ultimately, this approach has the potential of radically simplifying the data required to do outbreak analysis, as municipalities with limited public health funds may not monitor a large number of features needed for more extensive machine learning approaches.
This study investigated a model to assess the role of climate fluctuations on dengue (DENV) dynamics from 2010 to 2019 in four Brazilian municipalities. The proposed transmission model was based on a preexisting SEI-SIR model, but also incorporates the vector vertical transmission and the vector’s egg compartment, thus allowing rainfall to be introduced to modulate egg-hatching. Temperature and rainfall satellite data throughout the decade were used as climatic model inputs. A sensitivity analysis was performed to understand the role of each parameter. The model-simulated scenario was compared to the observed dengue incidence and the findings indicate that the model was able to capture the observed seasonal dengue incidence pattern with good accuracy until 2016, although higher deviations were observed from 2016 to 2019. The results further demonstrate that vertical transmission fluctuations can affect attack transmission rates and patterns, suggesting the need to investigate the contribution of vertical transmission to dengue transmission dynamics in future assessments. The improved understanding of the relationship between different environment variables and dengue transmission achieved by the proposed model can contribute to public health policies regarding mosquito-borne diseases.
Environmental changes are among the main factors that contribute to the emergence or re-emergence of viruses of public health importance. Here, we show the impact of environmental modifications on cases of infections by the dengue, chikungunya and Zika viruses in humans in the state of Tocantins, Brazil, between the years 2010 and 2019. We conducted a descriptive and principal component analysis (PCA) to explore the main trends in environmental modifications and in the cases of human infections caused by these arboviruses in Tocantins. Our analysis demonstrated that the occurrence of El Niño, deforestation in the Cerrado and maximum temperatures had correlations with the cases of infections by the Zika virus between 2014 and 2016. El Niño, followed by La Niña, a gradual increase in precipitation and the maximum temperature observed between 2015 and 2017 were shown to have contributed to the infections by the chikungunya virus. La Niña and precipitation were associated with infections by the dengue virus between 2010 and 2012 and El Niño contributed to the 2019 outbreak observed within the state. By PCA, deforestation, temperatures and El Niño were the most important variables related to cases of dengue in humans. We conclude from this analysis that environmental changes (deforestation and climate change) presented a strong influence on the human infections caused by the dengue, chikungunya and Zika viruses in Tocantins from 2010 to 2019.
Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007-2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.
According to the World Health Organization, dengue is a neglected tropical disease. Latin America, specifically Colombia is in alert regarding this arbovirosis as there was a spike in the number of reported dengue cases at the beginning of 2019. Although there has been a worldwide decrease in the number of reported dengue cases, Colombia has shown a growing trend over the past few years. This study performed a Poisson multilevel analysis with mixed effects on STATA® version 16 and R to assess sociodemographic, climatic, and entomological factors that may influence the occurrence of dengue in three municipalities for the period 2010-2015. Information on dengue cases and their sociodemographic variables was collected from the National Public Health Surveillance System (SIVIGILA) records. For climatic variables (temperature, relative humidity, and precipitation), we used the information registered by the weather stations located in the study area, which are managed by the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (IDEAM) or the Corporación Autónoma Regional (CAR). The entomological variables (house index, container index, and Breteau index) were provided by the Health office of the Cundinamarca department. SIVIGILA reported 1921 dengue cases and 56 severe dengue cases in the three municipalities; of them, three died. One out of four cases occurred in rural areas. The age category most affected was adulthood, and there were no statistical differences in the number of cases between sexes. The Poisson multilevel analysis with the best fit model explained the presentation of cases were temperature, relative humidity, precipitation, childhood, live in urban area and the contributory healthcare system. The temperature had the biggest influence on the presentation of dengue cases in this region between 2010 and 2015.
Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.
BACKGROUND: The influence of climate on the epidemiology of dengue has scarcely been studied in Cartagena. METHODS: The relationship between dengue cases and climatic and macroclimatic factors was explored using an ecological design and bivariate and time-series analyses during lag and non-lag months. Data from 2008-2017 was obtained from the national surveillance system and meteorological stations. RESULTS: Cases correlated only with climatic variables during lag and non-lag months. Decreases in precipitation and humidity and increases in temperature were correlated with an increase in cases. CONCLUSIONS: Our findings provide useful information for establishing and strengthening dengue prevention and control strategies.
West Nile virus (WNV, Flaviviridae, Flavivirus) is a mosquito-borne flavivirus introduced to North America in 1999. Since 1999, the Earth’s average temperature has increased by 0.6 °C. Mosquitoes are ectothermic organisms, reliant on environmental heat sources. Temperature impacts vector-virus interactions which directly influence arbovirus transmission. RNA viral replication is highly error-prone and increasing temperature could further increase replication rates, mutation frequencies, and evolutionary rates. The impact of temperature on arbovirus evolutionary trajectories and fitness landscapes has yet to be sufficiently studied. To investigate how temperature impacts the rate and extent of WNV evolution in mosquito cells, WNV was experimentally passaged 12 times in Culex tarsalis cells, at 25 °C and 30 °C. Full-genome deep sequencing was used to compare genetic signatures during passage, and replicative fitness was evaluated before and after passage at each temperature. Our results suggest adaptive potential at both temperatures, with unique temperature-dependent and lineage-specific genetic signatures. Further, higher temperature passage was associated with significantly increased replicative fitness at both temperatures and increases in nonsynonymous mutations. Together, these data indicate that if similar selective pressures exist in natural systems, increases in temperature could accelerate emergence of high-fitness strains with greater phenotypic plasticity.
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.
Dengue virus (DENV) is the causative agent of dengue fever and severe dengue. Every year, millions of people are infected with this virus. There is no vaccine available for this disease. Dengue virus is present in four serologically varying strains, DENV 1, 2, 3, and 4, and each of these serotypes is further classified into various genotypes based on the geographic distribution and genetic variance. Mosquitoes play the role of vectors for this disease. Tropical countries and some temperate parts of the world witness outbreaks of dengue mainly during the monsoon (rainy) seasons. Several algorithms have been developed to predict the occurrence and prognosis of dengue disease. These algorithms are mainly based on epidemiological data, climate factors, and online search patterns in the infected area. Most of these algorithms are based on either machine learning or deep learning techniques. We summarize the different software tools available for predicting the outbreaks of dengue based on the aforementioned factors, briefly outline the methodology used in these algorithms, and provide a comprehensive list of programs available for the same in this article.
Dengue, a mosquito-borne disease, poses a tremendous burden to human health with about 390 million annual dengue infections worldwide. The environmental temperature plays a major role in the mosquito life-cycle as well as the mosquito-human-mosquito dengue transmission cycle. While previous studies have provided useful insights into the understanding of dengue diseases, there is little emphasis put on the role of environmental temperature variation, especially diurnal variation, in the mosquito vector and dengue dynamics. In this study, we develop a mathematical model to investigate the impact of seasonal and diurnal temperature variations on the persistence of mosquito vector and dengue. Importantly, using a threshold dynamical system approach to our model, we formulate the mosquito reproduction number and the infection invasion threshold, which completely determine the global threshold dynamics of mosquito population and dengue transmission, respectively. Our model predicts that both seasonal and diurnal variations of the environmental temperature can be determinant factors for the persistence of mosquito vector and dengue. In general, our numerical estimates of the mosquito reproduction number and the infection invasion threshold show that places with higher diurnal or seasonal temperature variations have a tendency to suffer less from the burden of mosquito population and dengue epidemics. Our results provide novel insights into the theoretical understanding of the role of diurnal temperature, which can be beneficial for the control of mosquito vector and dengue spread.
Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.
Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.
BACKGROUND: Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. METHODOLOGY/PRINCIPAL FINDINGS: We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. CONCLUSIONS/SIGNIFICANCE: Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.
In this paper, we propose a new dynamical system model pertaining to Dengue transmission, and investigate its consequent morphology. We present and study various ramifications of our mathematical model for Dengue spread, encapsulated in a spatio-temporal differential system made of reaction-diffusion equations. Diffusion terms are incorporated into the said model by using specific derivations for infected mosquitoes, and infected humans, as well. Moreover, mechanisms for the nearest neighbor(s) infections are integrated into the model. Furthermore, using adaptive multigrid finite difference with decoupling and quasi-linearization techniques, we investigate two main factors for Dengue spatial propagation. We determine the effects of temperature variations, and the mobility of infectious agents, be they mosquitoes or humans. Finally, the proposed model-based analytico-numerical results are obtained, and rendered in graphical profiles, which show the major role the climate temperature and the mobility of infected humans have on the spread and speed of the disease. The consequent proposed model outcomes and health-based ramifications are then raised, discussed, and then validated.
BACKGROUND: Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints. OBJECTIVES: To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence. METHODS: Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence. RESULTS: The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas. CONCLUSIONS: Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.
Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.
The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind speed had the highest coefficients, i.e. β=0.17, β= -0.12 and β= -0.11 (P<0.001), respectively. The risk of dengue incidence occurring during the rainy season was almost twice as high as that during monsoon. Statistically significant spatial clusters of dengue cases were observed all through the province in different years. Nabon was identified as a hotspot, while Pak Phanang was a coldspot for dengue fever incidence, explained by the fact that the former is a rubber-plantation hub, while the agricultural plains of the latter lend themselves to the practice of pisciculture combined with rice farming. This information is imminently important for planning apt sustainable control measures for dengue epidemics.
BACKGROUND: Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. OBJECTIVE: This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. METHODS: Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). RESULTS AND DISCUSSION: LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. CONCLUSION: This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.
OBJECTIVE: The aim research was to analyze the association between temperature and humidity and the incidence of dengue fever in Manado Municipality. METHODS: The research design used analytical descriptive with a cross-sectional survey approach. Data were analyzed using the Spearman rank test. RESULT: The highest temperature was in August (28.7 °C), the highest humidity was January (88%), and the most DHF incidence was in January (409 cases). There is a significant association between temperature and the prevalence of DHF (p=0.000, r=-0.845). Humidity with the prevalence of DHF (p=0.000, r=0.873). CONCLUSION: It was found that two variables had a significant association between temperature and humidity on the prevalence of DHF in Manado Municipality based on observations of patterns of temperature and humidity characteristics every month during 2019.
BACKGROUND: Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. METHODS: The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. RESULTS: The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50-60% of dengue cases across the city. CONCLUSIONS: Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.
In 2014 and 2015, Southern Taiwan experienced two unprecedented outbreaks, with more than 10,000 laboratory-confirmed dengue cases in each outbreak. The present study was aimed to investigate the influence of meteorological and spatial factors on dengue outbreaks in Southern Taiwan and was conducted in Kaohsiung City, which is the most affected area in Taiwan. The distributed lag nonlinear model was used to investigate the role of climatic factors in the 2014 and 2015 dengue outbreaks. Spatial statistics in the Geographic Information System was applied to study the relationship between the dengue spreading pattern and locations of traditional markets (human motility) in the 2015 dengue outbreak. Meteorological analysis results suggested that the relative risk of dengue fever increased when the weekly average temperature was more than 15°C at lagged weeks 5 to 18. Elevated relative risk of dengue was observed when the weekly average rainfall was more than 150 mm at lagged weeks 12 to 20. The spatial analysis revealed that approximately 83% of dengue cases were located in the 1000 m buffer zone of traditional market, with statistical significance. These findings support the influence of climatic factors and human motility on dengue outbreaks. Furthermore, the study analysis may help authorities to identify hotspots and decide the timing for implementation of dengue control programs.
Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015-2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015-18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated β coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August-November).
The susceptibility of Asian tiger mosquitoes to DENV-2 in different seasons was observed in simulated field environments as a reference to design dengue fever control strategies in Guangzhou. The life table experiments of mosquitoes in four seasons were carried out in the field. The susceptibility of Ae. albopictus to dengue virus was observed in both environments in Guangzhou in summer and winter. Ae. albopictus was infected with dengue virus by oral feeding. On day 7 and 14 after infection, the viral load in the head, ovary, and midgut of the mosquito was detected using real-time fluorescent quantitative PCR. Immune-associated gene expression in infected mosquitoes was performed using quantitative real-time reverse transcriptase PCR. The hatching rate and pupation rate of Ae. albopictus larvae in different seasons differed significantly. The winter hatching rate of larvae was lower than that in summer, and the incubation time was longer than in summer. In the winter field environment, Ae. albopictus still underwent basic growth and development processes. Mosquitoes in the simulated field environment were more susceptible to DENV-2 than those in the simulated laboratory environment. In the midgut, viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 14.459, P = 0.01); ovarian viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 8.656, P < 0.001), but there was no significant difference in the viral load at other time points (P > 0.05). Dicer-2 mRNA expression on day 7 in winter was 4.071 times than that on day 7 in summer: the viral load and Dicer-2 expression correlated moderately. Ae. albopictus could still develop and transmit dengue virus in winter in Guangzhou. Mosquitoes under simulated field conditions were more susceptible to DENV-2 than those under simulated laboratory conditions.
Dengue is of great public health concern regarding the number of people affected. In addition, climate change is associated with the recent spread of dengue fever. Effects of meteorological factors on dengue incidence from 2003 to 2019 in Bangkok city: a model for dengue prediction. Mathematical statistical applied were principal component analysis (PCA), Poisson regression model (PRM), Mann-Kendall (MK), and Sen’s slope. PRM considers dengue incidence as the dependent variable and climate variables as independent variables. Meteorological factors are maximum temperature (T-max), minimum temperature (T-min), relative humidity (RH), and rainfall. The rainy season showed a high significant probability of occurrence for new patients. Most trends were statistically significant at 1% for seasonal and annual dengue cases. Another finding was that for every 5-50% of RH variation, there was an average increase (73.33-24,369.19%) in the number of dengue cases. Therefore, RH was the best predictor for increasing dengue incidence in Bangkok. In addition, predictions for dengue incidence were evaluated. This study is a significant result to warn the government, providing valuable information for human health protection.
OBJECTIVES: This study aimed to forecast the morbidity and mortality of dengue fever using a time series analysis from 2006 to 2016. METHODS: Data were compiled from the Jeddah Dengue Fever Operations Room (RFOR) in a primary health care centre. A time series analysis was conducted for all confirmed cases of dengue fever between 2006 and 2016. RESULTS: The results showed a significant seasonal association, particularly from May to September, and a time-varying behaviour. Air temperature was significantly associated with the incidence of dengue fever (p < 0.001) but was not correlated with its mortality. Similarly, relative humidity was not significantly associated with the incidence of dengue fever (p = 0.237). CONCLUSION: The strong seasonal association of dengue fever during May to September and its relation to air temperature should be communicated to all stakeholders. This will help improve the control interventions of dengue fever during periods of anticipated high incidence.
Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 degrees C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO2 days and low-precipitation on both higher PM2.5 and SO2 days were greater compared to the low-pollution days with P (interaction) being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO2 days and high-precipitation on higher PM2.5 d were smaller with P (interaction) being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM2.5 d while the impact of high-precipitation decreased.
BACKGROUND: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50-100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia. RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks. CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.
Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.
This study assessed the impact of weather factors, including novel predictors-pollutant standards index (PSI) and wind speed-on dengue incidence in Singapore between 2012 and 2019. Autoregressive integrated moving average (ARIMA) model was fitted to explore the autocorrelation in time series and quasi-Poisson model with a distributed lag non-linear term (DLNM) was set up to assess any non-linear association between climatic factors and dengue incidence. In DLNM, a PSI level of up to 111 was positively associated with dengue incidence; incidence reduced as PSI level increased to 160. A slight rainfall increase of up to 7 mm per week gave rise to higher dengue risk. On the contrary, heavier rainfall was protective against dengue. An increase in mean temperature under around 28.0 °C corresponded with increased dengue cases whereas the association became negative beyond 28.0 °C; the minimum temperature was significantly positively associated with dengue incidence at around 23-25 °C, and the relationship reversed when temperature exceed 27 °C. An overall positive association, albeit insignificant, was observed between maximum temperature and dengue incidence. Wind speed was associated with decreasing relative risk (RR). Beyond prevailing conclusions on temperature, this study observed that extremely poor air quality, high wind speed, minimum temperature 27 °C, and rainfall volume beyond 12 mm per week reduced the risk of dengue transmission in an urbanized tropical environment.
BACKGROUND: The mosquitoes Aedes aegypti and Ae. albopictus are the primary vectors of dengue virus, and their geographic distributions are predicted to expand further with economic development, and in response to climate change. We aimed to estimate the impact of future climate change on dengue transmission through the development of a Suitable Conditions Index (SCI), based on climatic variables known to support vectorial capacity. We calculated the SCI based on various climate change scenarios for six countries in the Asia-Pacific region (Australia, China, Indonesia, The Philippines, Thailand and Vietnam). METHODS: Monthly raster climate data (temperature and precipitation) were collected for the period January 2005 to December 2018 along with projected climate estimates for the years 2030, 2050 and 2070 using Representative Concentration Pathway (RCP) 4·5, 6·0 and 8·5 emissions scenarios. We defined suitable temperature ranges for dengue transmission of between 17·05-34·61 °C for Ae. aegypti and 15·84-31·51 °C for Ae. albopictus and then developed a historical and predicted SCI based on weather variability to measure the expected geographic limits of dengue vectorial capacity. Historical and projected SCI values were compared through difference maps for the six countries. FINDINGS: Comparing different emission scenarios across all countries, we found that most South East Asian countries showed either a stable pattern of high suitability, or a potential decline in suitability for both vectors from 2030 to 2070, with a declining pattern particularly evident for Ae. albopictus. Temperate areas of both China and Australia showed a less stable pattern, with both moderate increases and decreases in suitability for each vector in different regions between 2030 and 2070. INTERPRETATION: The SCI will be a useful index for forecasting potential dengue risk distributions in response to climate change, and independently of the effects of human activity. When considered alongside additional correlates of infection such as human population density and socioeconomic development indicators, the SCI could be used to develop an early warning system for dengue transmission.
The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1-3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022-1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620-0.903, p = 0.003), and most of the impact was across 0-3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120-0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.
BACKGROUND: Global incidence of dengue has surged rapidly over the past decade. Each year, an estimated 390 million infections occur worldwide, with Asia-Pacific countries bearing about three-quarters of the global dengue disease burden. Global warming may influence the pattern of dengue transmission. While previous studies have shown that extremely high temperatures can impede the development of the Aedes mosquito, the effect of such extreme heat over a sustained period, also known as heatwaves, has not been investigated in a tropical climate setting. AIM: We examined the short-term relationships between maximum ambient temperature and heatwaves and reported dengue infections in Singapore, via ecological time series analysis, using data from 2009 to 2018. METHODS: We studied the effect of two measures of extreme heat – (i) heatwaves and (ii) maximum ambient temperature. We used a negative binomial regression, coupled with a distributed lag nonlinear model, to examine the immediate and lagged associations of extreme temperature on dengue infections, on a weekly timescale. We adjusted for long-term trend, seasonality, rainfall and absolute humidity, public holidays and autocorrelation. RESULTS: We observed an overall inhibitive effect of heatwaves on the risk of dengue infections, and a parabolic relationship between maximum temperature and dengue infections. A 1 °C increase in maximum temperature from 31 °C was associated with a 13.1% (Relative Risk (RR): 0.868, 95% CI: 0.798, 0.946) reduction in the cumulative risk of dengue infections over six weeks. Weeks with 3 heatwave days were associated with a 28.3% (RR: 0.717, 95% CI: 0.608, 0.845) overall reduction compared to weeks with no heatwave days. Adopting different heatwaves specifications did not substantially alter our estimates. CONCLUSION: Extreme heat was associated with decreased dengue incidence. Findings from this study highlight the importance of understanding the temperature dependency of vector-borne diseases in resource planning for an anticipated climate change scenario.
BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available. METHODS: We used a statistical estimation of the effective reproduction number (R(t)) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios. RESULTS: Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same. CONCLUSION: We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available.
BACKGROUND: Studies have shown that tropical cyclones are associated with several infectious diseases, while very few evidence has demonstrated the relationship between tropical cyclones and dengue fever. This study aimed to examine the potential impact of tropical cyclones on dengue fever incidence in the Pearl River Delta, China. METHODS: Data on daily dengue fever incidence, occurrence of tropical cyclones and meteorological factors were collected between June and October, 2013-2018 from nine cities in the Pearl River Delta. Multicollinearity of meteorological variables was examined via Spearman correlation, variables with strong correlation (r>0.7) were not included in the model simultaneously. A time-stratified case-crossover design combined with conditional Poisson regression model was performed to evaluate the association between tropical cyclones and dengue fever incidence. Stratified analyses were performed by intensity grades of tropical cyclones (tropical storm and typhoon), sex (male and female) and age-groups (<18, 18-59, ≥60 years). RESULTS: During the study period, 20 tropical cyclones occurred and 47,784 dengue fever cases were reported. Tropical cyclones were associated with an increased risk of dengue fever in the Pearl River Delta region, with the largest relative risk of 1.62 with the 95% confidence interval (1.45-1.80) occurring on the lag 5 day. The strength of association was greater and lasted longer for typhoon than for tropical storm. There was no difference in effect estimates between males and females. However, individuals aged over 60 years were more vulnerable than others. CONCLUSIONS: Tropical cyclones are associated with increased risk of local dengue fever incidence in south China, with the elderly more vulnerable than other population subgroups. Health protective strategies should be developed to reduce the potential risk of dengue epidemic after tropical cyclones.
The identification of the key factors influencing dengue occurrence is critical for a successful response to the outbreak. It was interesting to consider possible differences in meteorological factors affecting dengue incidence during epidemic and non-epidemic periods. In this study, the overall correlation between weekly dengue incidence rates and meteorological variables were conducted in southern Taiwan (Tainan and Kaohsiung cities) from 2007 to 2017. The lagged-time Poisson regression analysis based on generalized estimating equation (GEE) was also performed. This study found that the best-fitting Poisson models with the smallest QICu values to characterize the relationships between dengue fever cases and meteorological factors in Tainan (QICu = −8.49 × 10−3) and Kaohsiung (−3116.30) for epidemic periods, respectively. During dengue epidemics, the maximum temperature with 2-month lag (β = 0.8400, p < 0.001) and minimum temperature with 5-month lag (0.3832, p < 0.001). During non-epidemic periods, the minimum temperature with 3-month lag (0.1737, p < 0.001) and mean temperature with 2-month lag (2.6743, p < 0.001) had a positive effect on dengue incidence in Tainan and Kaohsiung, respectively.
Hong Kong is an Asia-Pacific City with low incidence but periodic local outbreaks of dengue. A mixed-method assessment of the risk of expansion of dengue endemicity in such setting was conducted. Archived blood samples of healthy adult blood donors were tested for anti-dengue virus IgG at 2 time-points of 2014 and 2018/2019. Data on the monthly notified dengue cases, meteorological and vector (ovitrap index) variables were collected. The dengue virus (DENV) IgG seroprevalence of healthy adults in 2014 was 2.2% (95%C.I. = 1.8-2.8%, n = 3827) whereas that in 2018/2019 was 1.7% (95%C.I. = 1.2-2.3%, n = 2320). Serotyping on 42 sera in 2018/2019 showed that 22 (52.4%) were DENV-2. In 2002-2019, importation accounted for 95.3% of all reported cases. By wavelet analysis, local cases were in weak or no association with meteorological and vector variables. Without strong association between local cases and meteorological/vector variables, there was no evidence of increasing level of dengue infection in Hong Kong.
BACKGROUND: Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. OBJECTIVES: We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. METHODS: A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. RESULTS: We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation. CONCLUSIONS: Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.
Transmission of dengue virus is a complex process with interactions between virus, mosquitoes and humans, influenced by multiple factors simultaneously. Studies have examined the impact of climate or socio-ecological factors on dengue, or only analyzed the individual effects of each single factor on dengue transmission. However, little research has addressed the interactive effects by multiple factors on dengue incidence. This study uses the geographical detector method to investigate the interactive effect of climate and socio-ecological factors on dengue incidence from two perspectives: over a long-time series and during outbreak periods; and surmised on the possibility of dengue outbreaks in the future. Results suggest that the temperature plays a dominant role in the long-time series of dengue transmission, while socio-ecological factors have great explanatory power for dengue outbreaks. The interactive effect of any two factors is greater than the impact of single factor on dengue transmission, and the interactions of pairs of climate and socio-ecological factors have more significant impact on dengue. Increasing temperature and surge in travel could cause dengue outbreaks in the future. Based on these results, three recommendations are offered regarding the prevention of dengue outbreaks: mitigating the urban heat island effect, adjusting the time and frequency of vector control intervention, and providing targeted health education to travelers at the border points. This study hopes to provide meaningful clues and a scientific basis for policymakers regarding effective interventions against dengue transmission, even during outbreaks.
As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011-2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.
BACKGROUND: Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods. METHODS: The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions. RESULTS: We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou. CONCLUSION: A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.
BACKGROUND: In recent years, frequent outbreaks of dengue fever (DF) have become an increasingly serious public health issue in China, especially in the Pearl River Delta (PRD) with fast socioeconomic developments. Previous studies mainly focused on the historic DF epidemics, their influencing factors, and the prediction of DF risks. However, the future risks of this disease under both different socioeconomic development and representative concentration pathways (RCPs) scenarios remain little understood. METHODOLOGY AND PRINCIPAL FINDINGS: In this study, a spatial dataset of gross domestic product (GDP), population density, and land use and land coverage (LULC) in 2050 and 2070 was obtained by simulation based on the different shared socioeconomic pathways (SSPs), and the future climatic data derived from the RCP scenarios were integrated into the Maxent models for predicting the future DF risk in the PRD region. Among all the variables included in this study, socioeconomics factors made the dominant contribution (83% or so) during simulating the current spatial distribution of the DF epidemics in the PRD region. Moreover, the spatial distribution of future DF risk identified by the climatic and socioeconomic (C&S) variables models was more detailed than that of the climatic variables models. Along with global warming and socioeconomic development, the zones with DF high and moderate risk will continue to increase, and the population at high and moderate risk will reach a maximum of 48.47 million (i.e., 63.78% of the whole PRD) under the RCP 4.5/SSP2 in 2070. CONCLUSIONS: The increasing DF risk may be an inevitable public health threat in the PRD region with rapid socioeconomic developments and global warming in the future. Our results suggest that curbs in emissions and more sustainable socioeconomic growth targets offer hope for limiting the future impact of dengue, and effective prevention and control need to continue to be strengthened at the junction of Guangzhou-Foshan, north-central Zhongshan city, and central-western Dongguan city. Our study provides useful clues for relevant hygienic authorities making targeted adapting strategies for this disease.
The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman’s rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran’s I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.
Dengue fever is a leading cause of illness and death in the tropics and subtropics, and the disease has become a threat to many nonendemic countries where the competent vectors such as Aedes albopictus and Aedes aegypti are abundant. The dengue epidemic in Tokyo, 2014, poses the critical importance to accurately model and predict the outbreak risk of dengue fever in nonendemic regions. Using climatological datasets and traveler volumes in Japan, where dengue was not seen for 70 years by 2014, we investigated the outbreak risk of dengue in 47 prefectures, employing the temperature-dependent basic reproduction number and a branching process model. Our results show that the effective reproduction number varies largely by season and by prefecture, and, moreover, the probability of outbreak if an untraced case is imported varies greatly with the calendar time of importation and location of destination. Combining the seasonally varying outbreak risk with time-dependent traveler volume data, the unconditional outbreak risk was calculated, illustrating different outbreak risks between southern coastal areas and northern tourist cities. As the main finding, the large travel volume with nonnegligible risk of outbreak explains the reason why a summer outbreak in Tokyo, 2014, was observed. Prefectures at high risk of future outbreak would be Tokyo again, Kanagawa or Osaka, and highly populated prefectures with large number of travelers.
Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman’s correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
