Climate change and malaria transmission

The effect of explicit convection on simulated malaria transmission across Africa

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Modeling the effect of climatic conditions and topography on malaria incidence using Poisson regression: a Retrospective study in Bannu, Khyber Pakhtunkhwa, Pakistan

Background

Malaria has been identified as a crucial vector-borne disease around the globe. The primary aim of this study was to investigate the incidence of malaria in the district of Bannu and its relationship with climatic conditions such as temperature, rainfall, relative humidity, and topography.

Methods

Secondary data were obtained from the metrological office and government hospitals across the district for 5 years (2013-2017). A Poisson regression model was applied for the statistical analysis.

Results and discussion

The number of reported cases of malaria was 175,198. The regression analysis showed that temperature, relative humidity, and rainfall had a significant association (p < 0.05) with malaria incidence. In addition, the topographic variables were significantly associated (p < 0.05) with malaria incidence in the region. The percent variation in the odds ratio of incidence was 4% for every unit increase in temperature and 2% in humidity. In conclusion, this study indicated that the temperature, humidity, rainfall, and topographic variables were significantly associated with the incidence of malaria. Effective malaria control and interventions integrated with climatic factors must be considered to overcome the disease burden.

Assessment of malaria risk in Southeast Asia: a systematic review

BACKGROUND

Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas.

METHODS

A search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified.

RESULTS

This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence.

CONCLUSIONS

Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

Changes in the associations between malaria incidence and climatic factors across malaria endemic countries in Africa and Asia-Pacific region

Empirical evidence supporting the associations between malaria incidence and climatic factors has remained controversial, buffering the progress in the Global Malaria Program that aims to eliminate 90% of the world malaria burden by 2030. This study has aimed to evaluate the nature and extent at which these relations are maintained across all the malaria endemic countries of Africa and Asia-Pacific region. We have utilized the last two decades of malaria incidence, annual minimum temperature, and annual precipitation time series data (2000-2020) for the two most malaria-impacted regions. These data were fitted in the generalized linear model and the mixed effects model. The results showed that there exists a large heterogeneity in malaria incidence across the countries, and between the regions. Last two decadal tendencies showed significant reductions in the disease burden in almost all the countries in the Asia Pacific, with several exceptions or relatively slowed reductions in the Africa. We found significant changes in the positive linear associations between malaria incidence, annual minimum temperature, and annual precipitation across African countries. Many Asia-Pacific countries namely Bangladesh, Bhutan, Indonesia, South Korea, Nepal, Thailand, Vietnam showed negative effects in the associations with the annual precipitation. This study indicates that increasing temperature within the range of 12-30 °C can generate positive effects on malaria incidence, but the nature and extent of precipitation effects vary across countries and at a large regional scale.

Malaria hotspots and climate change trends in the hyper-endemic malaria settings of Mizoram along the India-Bangladesh borders

India has made tremendous progress in reducing malaria mortality and morbidity in the last decade. Mizoram State in North-East India is one of the few malaria-endemic regions where malaria transmission has continued to remain high. As Mizoram shares international borders with Bangladesh and Myanmar, malaria control in this region is critical for malaria elimination efforts in all the three countries. For identifying hotspots for targeted intervention, malaria data from 385 public health sub-centers across Mizoram were analyzed in the Geographic Information System. Almost all the sub-centers reporting high Annual Parasite Index (> 10) are located in Mizoram's districts that border Bangladesh. Getis-Ord G_i* statistic shows most of the sub-centers located along the Bangladesh border in the Lawngtlai and Lunglei districts to be the malaria hotspots. The hotspots also extended into the Mamit and Siaha districts, especially along the borders of Lawngtlai and Lunglei. Analysis of terrain, climatic, and land use/land cover datasets obtained from the Global Modelling and Assimilation Office and satellite images show Mizoram's western part (Lawngtlai, Lunglei, and Mamit districts) to experience similar topographic and climatic conditions as the bordering Rangamati district in the Chittagong division of Bangladesh. Climatic trends in this region from 1981 to 2021, estimated by the Mann-Kendall test and Sen's slope estimates, show an increasing trend in minimum temperature, relative humidity, rainfall, and the associated shift of climatic pattern (temperate to tropical monsoon) could facilitate malaria transmission. The quasi-Poisson regression model estimates a strong association (p < 0.001) between total malaria cases, temperature range, and elevation. The Kruskal-Wallis H test shows a statistically significant association between malaria cases and forest classes (p < 0.001). A regional coordination and strategic plan are required to eliminate malaria from this hyper-endemic malaria region of North-East India.

The impact of temperature and decay in insecticide-treated net efficacy on malaria prevalence and control

Insecticide-treated nets (ITNs) have been useful and effective in mitigating the risk of malaria globally. However, due to misuse and normal/human-induced physical and chemical wear, the effectiveness of ITNs in combating malaria has been declining. Underlying heterogeneities in the nature of malaria, combined with environmental factors such as temperature lead to complex malaria transmission and control dynamics. In particular, temperature plays a significant role in determining the risk of malaria since it influences the growth and survival of mosquitoes and the malaria parasite. Here, a unifying mechanistic framework that integrates malaria dynamics with waning ITN-efficacy and temperature change is developed and used to assess the impact of interactions between significant sources of variation (e.g., temperature) and waning ITN-efficacy on the risk of malaria transmission and the success of ITN programs. The model exhibits a backward bifurcation when ITN-efficacy is constant implying that control efforts must be stepped up and sustained a bit longer even when the reproduction number is slightly less than one. The study shows that malaria is more effectively controlled with ITNs that have a longer lifespan and if ITNs are replaced before the end of their expiration period. Also, failing to account for waning ITN-efficacy leads to an underestimation of disease risk, burden, and effort level required to contain the disease. Local and global sensitivity analyses show that control and temperature-related parameters are primary drivers of the reproduction number and the human disease burden, highlighting the significance of temperature on malaria dynamics. Furthermore, the study shows that the human disease burden is optimal at a temperature of ≈28°C and that high seasonal variations can trigger major malaria outbreaks even in regions with low mean temperatures. Additionally, accounting for both seasonality and decay in ITN-efficacy leads to complex malaria patterns. To sum it up, insights into the sensitivity of malaria dynamics on temperature are useful in assessing the potential impact of changes in temperature on malaria risk. Also, a malaria control program, which ensures that ITNs are replaced regularly and early enough, and that educates at risk populations on proper use and care for ITNs is necessary for reducing the burden of malaria.

Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo

BACKGROUND

Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

METHODS

Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis.

RESULTS

The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001).

CONCLUSIONS

The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

Molecular characterization and genotype distribution of thioester-containing protein 1 gene in Anopheles gambiae mosquitoes in western Kenya

BACKGROUND

Evolutionary pressures lead to the selection of efficient malaria vectors either resistant or susceptible to Plasmodium parasites. These forces may favour the introduction of species genotypes that adapt to new breeding habitats, potentially having an impact on malaria transmission. Thioester-containing protein 1 (TEP1) of Anopheles gambiae complex plays an important role in innate immune defenses against parasites. This study aims to characterize the distribution pattern of TEP1 polymorphisms among populations of An. gambiae sensu lato (s.l.) in western Kenya.

METHODS

Anopheles gambiae adult and larvae were collected using pyrethrum spray catches (PSC) and plastic dippers respectively from Homa Bay, Kakamega, Bungoma, and Kisumu counties between 2017 and 2020. Collected adults and larvae reared to the adult stage were morphologically identified and then identified to sibling species by PCR. TEP1 alleles were determined in 627 anopheles mosquitoes using restriction fragment length polymorphisms-polymerase chain reaction (RFLP-PCR) and to validate the TEP1 genotyping results, a representative sample of the alleles was sequenced.

RESULTS

Two TEP1 alleles (TEP1*S1 and TEP1*R2) and three corresponding genotypes (*S1/S1, *R2/S1, and *R2/R2) were identified. TEP1*S1 and TEP1*R2 with their corresponding genotypes, homozygous *S1/S1 and heterozygous *R2/S1 were widely distributed across all sites with allele frequencies of approximately 80% and 20%, respectively both in Anopheles gambiae and Anopheles arabiensis. There was no significant difference detected among the populations and between the two mosquito species in TEP1 allele frequency and genotype frequency. The overall low levels in population structure (F_ST = 0.019) across all sites corresponded to an effective migration index (Nm = 12.571) and low Nei's genetic distance values (< 0.500) among the subpopulation. The comparative fixation index values revealed minimal genetic differentiation between species and high levels of gene flow among populations.

CONCLUSION

Genotyping TEP1 has identified two common TEP1 alleles (TEP1*S1 and TEP1*R2) and three corresponding genotypes (*S1/S1, *R2/S1, and *R2/R2) in An. gambiae s.l. The TEP1 allele genetic diversity and population structure are low in western Kenya.

Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019

Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383−1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451−3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events.

Advances in Understanding Vector Behavioural Traits after Infection

Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can alter these behavioural traits of the arthropod vector. Here, we review relevant publications to assess how pathogens modulate the behaviour of mosquitoes and ticks, major vectors for human diseases. The research has shown that infection with pathogens alter the mosquito’s flight activity, mating, fecundity, host-seeking, blood-feeding, and adaptations to insecticide bed nets, and similarly modify the tick’s locomotion, questing heights, vertical and horizontal walks, tendency to overcome obstacles, and host-seeking ability. Although some of these behavioural changes may theoretically increase transmission potential of the pathogens, their effect on the disease epidemiology remains to be verified. This study will not only help in understanding virus–vector interactions but will also benefit in establishing role of these behavioural changes in improved epidemiological models and in devising new vector management strategies.

DNA identification of species of the Anopheles maculipennis complex and first record of An. daciae in Belgium

The present study aimed at identifying the members of the Anopheles maculipennis complex (Diptera: Culicidae) occurring in Belgium. Therefore, the second internal transcribed spacer of nuclear ribosomal DNA (ITS2) and the mitochondrial cytochrome oxidase subunit I (COI) loci were sequenced in 175 and 111 specimens, respectively, collected between 2007 and 2019. In parallel, the suitability of two species-diagnostic PCR-RFLP assays was tested. The identified specimens included: An. maculipennis s.s. (N = 105), An. daciae (N = 62), An. atroparvus (N = 6) and An. messeae (N = 2). Each species was characterized by unique ITS2 haplotypes, whereas COI only supported the monophyly of An. atroparvus, a historical malaria vector in Belgium. Species identification results were further supported by unique PCR-RFLP banding patterns. We report for the first time An. daciae in Belgium, where it was found to co-occur with An. maculipennis s.s. The latter was the most prevalent in the collection studied (60%) and appears to have the widest distribution in Belgium. As in other studies, An. daciae and An. messeae appeared the most closely related species, up to the point that their species status remains debatable, while their ecological differences, including vector competences, need further study.

Surveillance of Chigger Mite Vectors for Tsutsugamushi Disease in the Hwaseong Area, Gyeonggi-do, Republic of Korea, 2015

Owing to global climate change, the global resurgence of vector-borne infectious diseases and their potential to inflict widespread casualties among human populations has emerged as a pivotal burden on public health systems. Tsutsugamushi disease (scrub typhus) in the Republic of Korea is steadily increasing and was designated as a legal communicable disease in 1994. The disease is a mite-borne acute febrile disease most commonly contracted from October to December. In this study, we tried to determine the prevalence of tsutsugamushi disease transmitted by chigger mites living on rodents and investigated their target vector diversity, abundance, and distribution to enable the mapping of hotspots for this disease in 2015. A total of 5 species belonging to 4 genera (109 mites): Leptotrombidium scutellare 60.6%, L. pallidum 28.4% Neotrombicula tamiyai 9.2%, Euschoengastia koreaensis/0.9%), and Neoschoengastia asakawa 0.9% were collected using chigger mite collecting traps mimicking human skin odor and sticky chigger traps from April to November 2015. Chigger mites causing tsutsugamushi disease in wild rodents were also collected in Hwaseong for the zoonotic surveillance of the vector. A total of 77 rodents belonging to 3 genera: Apodemus agrarius (93.5%), Crocidura lasiura (5.2%), and Micromys minutus (1.3%) were collected in April, October, and November 2015. The most common mite was L. pallidum (46.9%), followed by L. scutellare (18.6%), and L. orientale (18.0%). However, any of the chigger mite pools collected from rodent hosts was tested positive for Orientia tsutsugamushi, the pathogen of tsutsugamushi disease, in this survey.

Drivers of community turnover differ between avian hemoparasite genera along a North American latitudinal gradient

The latitudinal diversity gradient (LDG) is an established macroecological pattern, but is poorly studied in microbial organisms, particularly parasites. In this study, we tested whether latitude, elevation, and host species predicted patterns of prevalence, alpha diversity, and community turnover of hemosporidian parasites. We expected parasite diversity to decrease with latitude, alongside the diversity of their hosts and vectors. Similarly, we expected infection prevalence to decrease with latitude as vector abundances decrease. Lastly, we expected parasite community turnover to increase with latitudinal distance and to be higher between rather than within host species. We tested these hypotheses by screening blood and tissue samples of three closely related avian species in a clade of North American songbirds (Turdidae: Catharus, n = 466) across 17.5° of latitude. We used a nested PCR approach to identify parasites in hemosporidian genera that are transmitted by different dipteran vectors. Then, we implemented linear-mixed effects and generalized dissimilarity models to evaluate the effects of latitude, elevation, and host species on parasite metrics. We found high diversity of hemosporidian parasites in Catharus thrushes (n = 44 lineages) but no evidence of latitudinal gradients in alpha diversity or prevalence. Parasites in the genus Leucocytozoon were most prevalent and lineage rich in this study system; however, there was limited turnover with latitude and host species. Contrastingly, Plasmodium parasites were less prevalent and diverse than Leucocytozoon parasites, yet communities turned over at a higher rate with latitude and host species. Leucocytozoon communities were skewed by the dominance of one or two highly prevalent lineages with broad latitudinal distributions. The few studies that evaluate the hemosporidian LDG do not find consistent patterns of prevalence and diversity, which makes it challenging to predict how they will respond to global climate change.

Modeling an association between malaria cases and climate variables for Keonjhar district of Odisha, India: a Bayesian approach

Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R² (RsqB). Whereas, the predictive ability of the model is measured using a cross-validation technique. As a result, it is found that the square root of malaria cases with lag 1, maximum temperature, and relative humidity with lag 3 and 0 (respectively), are significantly positively associated with the square root of the cases. However, the minimum and average temperatures with lag 2, respectively, are observed as negatively (significantly) related. The considered model accounts for moderate amount of variation in the square root of malaria cases as received through the results for RsqB. We also present Absolute Percentage Errors (APE) for each of the 12 months (January-December) for a better understanding of the seasonal pattern of the predicted (square root of) malaria cases. Most of the APEs obtained corresponding to test data points is reasonably low. Further, the analysis shows that the considered model closely predicted the actual (square root of) malaria cases, except for some peak cases during the particular months. The output of the current research might help the district to develop and strengthen early warning prediction of malaria cases for proper mitigation, eradication, and prevention in similar settings.

Current Challenges in the Development of Vaccines and Drugs Against Emerging Vector-borne Diseases

Vectors are living organisms that transmit infectious diseases from an infected animal to humans or another animal. Biological vectors such as mosquitoes, ticks, and sand flies carry pathogens that multiply within their bodies prior to delivery to a new host. The increased prevalence of Vector-Borne Diseases (VBDs) such as Aedes-borne dengue, Chikungunya (CHIKV), Zika (ZIKV), malaria, Tick-Borne Disease (TBD), and scrub typhus has a huge impact on the health of both humans and livestock worldwide. In particular, zoonotic diseases transmitted by mosquitoes and ticks place a considerable burden on public health. Vaccines, drugs, and vector control methods have been developed to prevent and treat VBDs and have prevented millions of deaths. However, development of such strategies is falling behind the rapid emergence of VBDs. Therefore, a comprehensive approach to fighting VBDs must be considered immediately. In this review, I focus on the challenges posed by emerging outbreaks of VBDs and discuss available drugs and vaccines designed to overcome this burden. Research into promising drugs needs to be upgraded and fast-tracked, and novel drugs or vaccines being tested in in vitro and in vivo models need to be moved into human clinical trials. Active preventive tactics, as well as new and upgraded diagnostics, surveillance, treatments, and vaccination strategies, need to be monitored constantly if we are to manage VBDs of medical importance.

Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions

The future implications of climate change on malaria transmission at the global level have already been reported, however such evidences are scarce and limited in India. Here our study aims to assess, identify and map the potential effects of climate change on Plasmodium vivax (Pv) and Plasmodium falciparum (Pf) malaria transmission in India. A Fuzzy-based Climate Suitability Malaria Transmission (FCSMT) model under the GIS environment was generated using Temperature and Relative Humidity data, extracted from CORDEX South Asia for Baseline (1976-2005) and RCP 4.5 scenario for future projection by the 2030s (2021-2040). National malaria data were used at the model analysis stage. Model outcomes suggest that climate change may significantly increase the spatial spread of Pv and Pf malaria with a numerical increase in the transmission window's (TW) months, and a shift in the months of transmission. Some areas of the western Himalayan states are likely to have new foci of Pv malaria transmission. Interior parts of some southern and eastern states are likely to become more suitable for Pf malaria transmission. Study has also identified the regions with a reduction in transmission months by the 2030s, leading to unstable malaria, and having the potential for malaria outbreaks.

Climatic Conditions: Conventional and Nanotechnology-Based Methods for the Control of Mosquito Vectors Causing Human Health Issues

Climate variability is highly impacting on mosquito-borne diseases causing malaria and dengue fever across the globe. Seasonal variability change in temperature and rainfall patterns are impacting on human health. Mosquitoes cause diseases like dengue fever, yellow fever, malaria, Chikungunya, West Nile and Japanese encephalitis. According to estimations by health organizations, annually one million human deaths are caused by vector-borne diseases, and dengue fever has increased about 30-fold over the past 50 years. Similarly, over 200 million cases of malaria are being reported annually. Mosquito-borne diseases are sensitive to temperature, humidity and seasonal variability. Both conventional (environmental, chemical, mechanical, biological etc.) and nanotechnology-based (Liposomes, nano-suspensions and polymer-based nanoparticles) approaches are used for the eradication of Malaria and dengue fever. Now green approaches are used to eradicate mosquitoes to save human health without harming the environment. In this review, the impact of climatic conditions on mosquito-borne diseases along with conventional and nanotechnology-based approaches used for controlling malaria and dengue fever have been discussed. Important recommendations have been made for people to stay healthy.

Impact of Temperature Variant on Survival of Aedes albopictus Skuse (Diptera: Culicidae): Implications on Thermotolerance and Acclimation

Aedes albopictus (Skuse 1894) is prevalent in the urban/peri-urban Port Blair, posing a public health threat, during past outbreaks of chikungunya (2006) and dengue (2010). Despite its vector potential, information on the biology is scanty. Therefore, impact of temperature on survival of immature stages, under laboratory conditions, was studied on F₁ population of Andamans. Ae. albopictus larvae were exposed to static temperatures viz. 37°C, 39°C, 41°C, 43°C and 45°C, and the lethal time to cause 50% (LT₅₀) and 90% mortality (LT₉₀) was computed. To assess adaptive thermotolerance, larvae exposed (37°C and 39°C) were re-exposed to higher temperatures (43°C and 45°C). All larvae survived at 37°C and 39°C for the entire exposure period of 420 min, while variable mortality was observed at 41°C, 43°C and 45°C. Larvae re-exposed to 43°C and 45°C showed an increase in thermotolerance with respect to non-adapted larvae. The results are discussed in the context of survival, development and distribution.

Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence

Background

The climate variables that directly influence vector-borne diseases’ ecosystems are mainly temperature and rainfall. This is not only because the vectors bionomics are strongly dependent upon these variables, but also because most of the elements of the systems are impacted, such as the host behavior and development and the pathogen amplification. The impact of the climate changes on the transmission patterns of these diseases is not easily understood, since many confounding factors are acting together. Consequently, knowledge of these impacts is often based on hypothesis derived from mathematical models. Nevertheless, some direct evidences can be found for several vector-borne diseases.

Main body

Evidences of the impact of climate change are available for malaria, arbovirus diseases such as dengue, and many other parasitic and viral diseases such as Rift Valley Fever, Japanese encephalitis, human African trypanosomiasis and leishmaniasis. The effect of temperature and rainfall change as well as extreme events, were found to be the main cause for outbreaks and are alarming the global community. Among the main driving factors, climate strongly influences the geographical distribution of insect vectors, which is rapidly changing due to climate change. Further, in both models and direct evidences, climate change is seen to be affecting vector-borne diseases more strikingly in fringe of different climatic areas often in the border of transmission zones, which were once free of these diseases with human populations less immune and more receptive. The impact of climate change is also more devastating because of the unpreparedness of Public Health systems to provide adequate response to the events, even when climatic warning is available. Although evidences are strong at the regional and local levels, the studies on impact of climate change on vector-borne diseases and health are producing contradictory results at the global level.

Conclusions

In this paper we discuss the current state of the results and draw on evidences from malaria, dengue and other vector-borne diseases to illustrate the state of current thinking and outline the need for further research to inform our predictions and response.

Electronic supplementary material

The online version of this article (10.1186/s40249-019-0565-1) contains supplementary material, which is available to authorized users.

Population Dynamics of Anopheles gambiae s.l. and Culex quinquefasciatus in Rural and Urban Settings Before an Indoor Residual Spraying Campaign in Northern Benin

Background: The purpose of this report is to provide information on Culicidae diversity; biting behavior and spatio-seasonal variation of abundance of Anopheles gambiae s.l. and Culex quinquefasciatus in rural and urban settings of the Alibori and Donga regions, Northern Benin, where an indoor residual spraying (IRS) campaign to control malaria is planned. Methods: Both human landing catches, associated with pyrethrum spray catches were used to monitor the mosquito populations in 12 sites with 1 urban and 1 rural located in each of the 6 districts randomly selected in the two targeted regions. After morphological identification of all mosquito specimens, biting behavior and density of An. gambiae s.l. and Cx quinquefasciatus were studied. PCR was also performed on An. gambiae s.l., to identify sibling species and its seasonal variation. Results: A total of 10,367 mosquitoes were captured, related to 14 species of the genera, Anopheles, Aedes, Culex and Mansonia. Of the total species collection, 40.39% were An. gambiae s.l. and 56.85% were Cx. quinquefasciatus. An. gambiae s.l. was more abundant in Donga (2521 specimens) compared with Alibori (1666 specimens). The opposite trend was observed with Cx. quinquefasciatus (2162 specimens in Donga against 4028 in Alibori). An. gambiae s.l. was predominant and displayed a higher blood feeding rate in rural areas, whereas Cx. quinquefasciatus was in majority in urban areas. An. gambiae s.l. was more endophagic, whereas Cx. quinquefasciatus showed similar indoor and outdoor biting behavior. An. gambiae s.l. was composed of An. coluzzii found in majority in the drought, and An. gambiae, which was predominant in the rainy season. Conclusion: The predominance of the malaria vector, An. gambiae s.l. and their higher blood feeding rate and their significantly high endophagy in rural areas indicate that these areas should be primarily targeted with the IRS operations to have a substantial impact on malaria transmission. Endophagy, characteristic of An. gambiae s.l. in our study area, suggests that IRS will have a positive impact on vector control if implemented 1 week before June that is the onset of the rainy season.

Assessing the role of climate factors on malaria transmission dynamics in South Sudan

Malaria is endemic in South Sudan and it is one of the most severe diseases in the war-torn nation. There has been much concern about whether the severity of its transmission might depend upon climatic conditions that are related to the reproduction of the single-cell parasite attaching to female mosquitoes, especially in high altitude areas. The country experiences two different climatic conditions; namely one tropical and the other hot and semi-arid. In this study, we aim to assess the potential impact of climatic conditions on malaria prevalence in these two climatically distinct regions of South Sudan. We develop and analyze a host-mosquito disease-based model that includes temperature and rainfall. The model has also been parameterized in a Bayesian framework using Bayesian Markov Chain Monte Carlo (MCMC). The mathematical analysis for this study has included equilibria, stability and a sensitivity index on the basic reproduction number R₀. The threshold R₀ is also used to provide a numerical basis for further refinement and prediction of the impact of climate variability on malaria transmission intensity over the study region. The study highlights the impact of various temperature values on the population dynamics of the mosquito.

Climate change and vector-borne diseases of public health significance

There has been much debate as to whether or not climate change will have, or has had, any significant effect on risk from vector-borne diseases. The debate on the former has focused on the degree to which occurrence and levels of risk of vector-borne diseases are determined by climate-dependent or independent factors, while the debate on the latter has focused on whether changes in disease incidence are due to climate at all, and/or are attributable to recent climate change. Here I review possible effects of climate change on vector-borne diseases, methods used to predict these effects and the evidence to date of changes in vector-borne disease risks that can be attributed to recent climate change. Predictions have both over- and underestimated the effects of climate change. Mostly under-estimations of effects are due to a focus only on direct effects of climate on disease ecology while more distal effects on society's capacity to control and prevent vector-borne disease are ignored. There is increasing evidence for possible impacts of recent climate change on some vector-borne diseases but for the most part, observed data series are too short (or non-existent), and impacts of climate-independent factors too great, to confidently attribute changing risk to climate change.

Temporal Dynamics of Phlebotomine Sand Flies Population in Response to Ambient Temperature Variation, Bam, Kerman Province of Iran

BACKGROUND

Variations in climate condition may have changed the dynamic of zoonotic cutaneous leishmaniasis (ZCL) and its agents such as sand flies and reservoir in the Bam Kerman the dry region of Iran.

OBJECTIVES

In this study we intend to examine the seasonal and interannual dynamics of the phlebotomine mosquito as a function of ambient temperature in Bam, Kerman one of the main leshmaniasis prevalence area in Iran.

METHODS

The MODIS land surface temperature product (LST; MODIS/Terra LST/E Monthly L3 Global 0.05Deg CMG [MOD11C3]) and land-based climatic data were used as explanatory variables. Monthly caught mosquitoes in Bam, Kerman, were used as a dependent variable. The temporal associations were first investigated by inspection of scatterplots and single-variable regression analysis. A multivariate linear regression model was developed to reveal the association between ambient temperature and the monthly mosquito abundance at a 95% confidence level (P < 0.05).

FINDINGS

The findings indicated that the monthly variation of 0-10 cm of soil depth temperature is the main driver of phlebotomine mosquito temporal dynamics. The developed multivariate model also indicated that the ambient temperature variation was responsible for >0.80 of temporal dynamics of phlebotomine mosquitos in Bam.

Mathematical modeling of climate change and malaria transmission dynamics: a historical review

Malaria, one of the greatest historical killers of mankind, continues to claim around half a million lives annually, with almost all deaths occurring in children under the age of five living in tropical Africa. The range of this disease is limited by climate to the warmer regions of the globe, and so anthropogenic global warming (and climate change more broadly) now threatens to alter the geographic area for potential malaria transmission, as both the Plasmodium malaria parasite and Anopheles mosquito vector have highly temperature-dependent lifecycles, while the aquatic immature Anopheles habitats are also strongly dependent upon rainfall and local hydrodynamics. A wide variety of process-based (or mechanistic) mathematical models have thus been proposed for the complex, highly nonlinear weather-driven Anopheles lifecycle and malaria transmission dynamics, but have reached somewhat disparate conclusions as to optimum temperatures for transmission, and the possible effect of increasing temperatures upon (potential) malaria distribution, with some projecting a large increase in the area at risk for malaria, but others predicting primarily a shift in the disease's geographic range. More generally, both global and local environmental changes drove the initial emergence of P. falciparum as a major human pathogen in tropical Africa some 10,000 years ago, and the disease has a long and deep history through the present. It is the goal of this paper to review major aspects of malaria biology, methods for formalizing these into mathematical forms, uncertainties and controversies in proper modeling methodology, and to provide a timeline of some major modeling efforts from the classical works of Sir Ronald Ross and George Macdonald through recent climate-focused modeling studies. Finally, we attempt to place such mathematical work within a broader historical context for the "million-murdering Death" of malaria.

Opposed elevational variation in prevalence and intensity of endoparasites and their vectors in a lizard

Studying the causes of parasite geographic distribution is relevant to understand ecological and evolutionary processes that affect host populations as well as for species conservation. Temperature is one of the most important environmental variables affecting parasite distribution, as raising temperatures positively affect development, reproduction, and rate of transmission of both endo- and ectoparasites. In this context, it is generally accepted that, in mountains, parasite abundance decreases with elevation. However, empirical evidence on this topic is limited. In the present study, we analyzed the elevational variation of hemoparasites and ectoparasites of a lizard, Psammodromus algirus, along a 2,200-m elevational gradient in Sierra Nevada (SE Spain). As predicted, ectoparasite (mites, ticks, mosquitoes, and sandflies) abundance decreased with elevation. However, hemoparasite prevalence and intensity in the lizard augmented with altitude, showing a pattern contrary to their vectors (mites). We suggest that tolerance to hemoparasites may increase with elevation as a consequence of lizards at high altitudes taking advantage of increased body condition and food availability, and reduced oxidative stress. Moreover, lizards could have been selected for higher resistance against hemoparasites at lowlands (where higher rates of replication are expected), thus reducing hemoparasite prevalence and load. Our findings imply that, in a scenario of climate warming, populations of lizards at high elevation may face increased abundance of ectoparasites, accompanied with strong negative effects.

Environmental Determinants of Malaria Transmission Around the Koka Reservoir in Ethiopia

New dam construction is known to exacerbate malaria transmission in Africa as the vectors of malaria-Anopheles mosquitoes-use bodies of water as breeding sites. Precise environmental mechanisms of how reservoirs exacerbate malaria transmission are yet to be identified. Understanding of these mechanisms should lead to a better assessment of the impacts of dam construction and to new prevention strategies. Combining extensive multiyear field surveys around the Koka Reservoir in Ethiopia and rigorous model development and simulation studies, environmental mechanisms of malaria transmission around the reservoir were examined. Most comprehensive and detailed malaria transmission model, Hydrology, Entomology, and Malaria Transmission Simulator, was applied to a village adjacent to the reservoir. Significant contributions to the dynamics of malaria transmission are shaped by wind profile, marginal pools, temperature, and shoreline locations. Wind speed and wind direction influence Anopheles populations and malaria transmission during the major and secondary mosquito seasons. During the secondary mosquito season, a noticeable influence was also attributed to marginal pools. Temperature was found to play an important role, not so much in Anopheles population dynamics, but in malaria transmission dynamics. Change in shoreline locations drives malaria transmission dynamics, with closer shoreline locations to the village making malaria transmission more likely. Identified environmental mechanisms help in predicting malaria transmission seasons and in developing village relocation strategies upon dam construction to minimize the risk of malaria.

Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá

BACKGROUND

Malaria has historically been entrenched in indigenous populations of the República de Panamá. This scenario occurs despite the fact that successful methods for malaria elimination were developed during the creation of the Panamá Canal. Today, most malaria cases in the República de Panamá affect the Gunas, an indigenous group, which mainly live in autonomous regions of eastern Panamá. Over recent decades several malaria outbreaks have affected the Gunas, and one hypothesis is that such outbreaks could have been exacerbated by climate change, especially by anomalous weather patterns driven by the EL Niño Southern Oscillation (ENSO).

RESULTS

Monthly malaria cases in Guna Yala (1998-2016) were autocorrelated up to 2 months of lag, likely reflecting parasite transmission cycles between humans and mosquitoes, and cyclically for periods of 4 months that might reflect relapses of Plasmodium vivax, the dominant malaria parasite transmitted in Panamá. Moreover, malaria case number was positively associated (P < 0.05) with rainfall (7 months of lag), and negatively with the El Niño 4 index (15 months of lag) and the Normalized Difference Vegetation Index, NDVI (8 months of lag), the sign and magnitude of these associations likely related to the impacts of weather patterns and vegetation on the ecology of Anopheles albimanus, the main malaria vector in Guna Yala. Interannual cycles, of approximately 4-year periods, in monthly malaria case numbers were associated with the El Niño 4 index, a climatic index associated with weather and vegetation dynamics in Guna Yala at seasonal and interannual time scales.

CONCLUSION

The results showed that ENSO, rainfall and NDVI were associated with the number of malaria cases in Guna Yala during the study period. These results highlight the vulnerability of Guna populations to malaria, an infection sensitive to climate change, and call for further studies about weather impacts on malaria vector ecology, as well as the association of malaria vectors with Gunas paying attention to their socio-economic conditions of poverty and cultural differences as an ethnic minority.

The associations between malaria, interventions, and the environment: a systematic review and meta-analysis

BACKGROUND

Malaria transmission is driven by multiple factors, including complex and multifaceted connections between malaria transmission, socioeconomic conditions, climate and interventions. Forecasting models should account for all significant drivers of malaria incidence although it is first necessary to understand the relationship between malaria burden and the various determinants of risk to inform the development of forecasting models. In this study, the associations between malaria risk, environmental factors, and interventions were evaluated through a systematic review.

METHODS

Five electronic databases (CAB Abstracts, EMBASE, Global Health, MEDLINE and ProQuest Dissertations & Theses) were searched for studies that included both the effects of the environment and interventions on malaria within the same statistical model. Studies were restricted to quantitative analyses and health outcomes of malaria mortality or morbidity, outbreaks, or transmission suitability. Meta-analyses were conducted on a subset of results using random-effects models.

RESULTS

Eleven studies of 2248 potentially relevant articles that met inclusion criteria were identified for the systematic review and two meta-analyses based upon five results each were performed. Normalized Difference Vegetation Index was not found to be statistically significant associated with malaria with a pooled OR of 1.10 (95% CI 0.07, 1.71). Bed net ownership was statistically associated with decreasing risk of malaria, when controlling for the effects of environment with a pooled OR of 0.75 (95% CI 0.60, 0.95). In general, environmental effects on malaria, while controlling for the effect of interventions, were variable and showed no particular pattern. Bed nets ownership, use and distribution, have a significant protective effect while controlling for environmental variables.

CONCLUSIONS

There are a limited number of studies which have simultaneously evaluated both environmental and interventional effects on malaria risk. Poor statistical reporting and a lack of common metrics were important challenges for this review, which must be addressed to ensure reproducibility and quality research. A comprehensive or inclusive approach to identifying malaria determinants using standardized indicators would allow for a better understanding of its epidemiology, which is crucial to improve future malaria risk estimations.

Spatial distribution and habitat characterization of mosquito species during the dry season along the Mara River and its tributaries, in Kenya and Tanzania

Background

Vector-borne diseases are increasingly becoming a major health problem among communities living along the major rivers of Africa. Although larger water bodies such as lakes and dams have been extensively researched, rivers and their tributaries have largely been ignored. This study sought to establish the spatial distribution of mosquito species during the dry season and further characterize their habitats along the Mara River and its tributaries.

Methods

In this cross-sectional survey, mosquito larvae were sampled along the Mara River, its two perennial tributaries (Amala and Nyangores), drying streams, and adjacent aquatic habitats (e.g. swamps, puddles that receive direct sunlight [open sunlit puddles], rock pools, hippo and livestock hoof prints, and vegetated pools). Each habitat was dipped 20 times using a standard dipper. Distance between breeding sites and human habitation was determined using global positioning system coordinates. The collected mosquito larvae were identified using standard taxonomic keys. Water physico-chemical parameters were measured in situ using a multiparameter meter. Mean mosquito larvae per habitat type were compared using analysis of variance and chi-square tests, while the relationship between mosquito larvae and physico-chemical parameters was evaluated using a generalized linear mixed model. The Cox-Stuart test was used to detect trends of mosquito larvae distribution. The test allowed for verification of monotonic tendency (rejection of null hypothesis of trend absence) and its variability.

Results

A total of 4001 mosquito larvae were collected, of which 2712 (67.8%) were collected from river/stream edge habitats and 1289 (32.2%) were sampled from aquatic habitats located in the terrestrial ecosystem about 50 m away from the main river/streams. Anopheles gambiae s.s, An. arabiensis, and An. funestus group, the three most potent vectors of malaria in Sub-Saharan Africa, together with other anopheline mosquitoes, were the most dominant mosquito species (70.3%), followed by Culex quinquefasciatus and Cx. pipiens complex combined (29.5%). Drying streams accounted for the highest number of larvae captured compared to the other habitat types. A stronger relationship between mosquito larvae abundance and dissolved oxygen (Z = 7.37, P ≤ 0.001), temperature (Z = 7.65, P ≤ 0.001), turbidity (Z = −5.25, P ≤ 0.001), and distance to the nearest human habitation (Z = 4.57, P ≤ 0.001), was observed.

Conclusions

Presence of malaria and non-malaria mosquito larvae within the Mara River basin calls for immediate action to curtail the insurgence of vector-borne diseases within the basin. A vector control program should be conducted during the dry period, targeting drying streams shown to produce the highest number of larval mosquitoes.

Electronic supplementary material

The online version of this article (10.1186/s40249-017-0385-0) contains supplementary material, which is available to authorized users.

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.

The Influence of Dams on Malaria Transmission in Sub-Saharan Africa

The construction of dams in sub-Saharan Africa is pivotal for food security and alleviating poverty in the region. However, the unintended adverse public health implications of extending the spatial distribution of water infrastructure are poorly documented and may minimize the intended benefits of securing water supplies. This paper reviews existing studies on the influence of dams on the spatial distribution of malaria parasites and vectors in sub-Saharan Africa. Common themes emerging from the literature were that dams intensified malaria transmission in semi-arid and highland areas with unstable malaria transmission but had little or no impact in areas with perennial transmission. Differences in the impacts of dams resulted from the types and characteristics of malaria vectors and their breeding habitats in different settings of sub-Saharan Africa. A higher abundance of a less anthropophilic Anopheles arabiensis than a highly efficient vector A. gambiae explains why dams did not increase malaria in stable areas. In unstable areas where transmission is limited by availability of water bodies for vector breeding, dams generally increase malaria by providing breeding habitats for prominent malaria vector species. Integrated vector control measures that include reservoir management, coupled with conventional malaria control strategies, could optimize a reduction of the risk of malaria transmission around dams in the region.

Modeling and analyzing the impact of temperature and rainfall on mosquito population dynamics over Kwazulu-Natal, South Africa

Malaria parasites are strongly dependent on Anopheles mosquitoes for transmission; for this reason, mosquito population dynamics are a crucial determinant of malaria risk. However, temperature and rainfall play a significant role in both aquatic and adult stages of the Anopheles. Consequently, it is important to understand the biology of malaria vector mosquitoes in the study of malaria transmission. In this study, we develop a climate-based, ordinary-differential-equation model to analyze how rainfall and temperature determine mosquito population size. In the model, we consider in detail the influence of ambient temperature on gonotrophic and sporogonic cycles over Amajuba District, Kwazulu-Natal Province, South Africa. In particular, we further use the model to simulate the spatial distribution of the mosquito biting rate over the study region. Our results reflect high seasonality of the population of An. gambiae over the region and also demonstrate the influence of climatic factors on the mosquito population dynamics.

El Niño Southern Oscillation as an early warning tool for malaria outbreaks in India

BACKGROUND

Risks of malaria epidemics in relation to El Niño and Southern Oscillation (ENSO) events have been mapped and studied at global level. In India, where malaria is a major public health problem, no such effort has been undertaken that inter-relates El Niño, Indian Summer Monsoon Rainfall (ISMR) and malaria. The present study has been undertaken to find out the relationship between ENSO events, ISMR and intra-annual variability in malaria cases in India, which in turn could help mitigate the malaria outbreaks.

METHODS

Correlation coefficients among 'rainfall index' (ISMR), '+ winter ONI' (NDJF) and 'malaria case index' were calculated using annual state-level data for the last 22 years. The 'malaria case index' representing 'relative change from mean' was correlated to the 4 month (November-February) average positive Oceanic Niño Index (ONI). The resultant correlations between '+ winter ONI' and 'malaria case index' were further analysed on geographical information system platform to generate spatial correlation map.

RESULTS

The correlation between '+ winter ONI' and 'rainfall index' shows that there is great disparity in effect of ENSO over ISMR distribution across the country. Correlation between 'rainfall index' and 'malaria case index' shows that malaria transmission in all geographical regions of India are not equally affected by the ISMR deficit or excess. Correlation between '+ winter ONI' and 'malaria case index' was found ranging from -0.5 to + 0.7 (p < 0.05). A positive correlation indicates that increase in El Niño intensity (+ winter ONI) will lead to rise in total malaria cases in the concurrent year in the states of Orissa, Chhattisgarh, Jharkhand, Bihar, Goa, eastern parts of Madhya Pradesh, part of Andhra Pradesh, Uttarakhand and Meghalaya. Whereas, negative correlations were found in the states of Rajasthan, Haryana, Gujarat, part of Tamil Nadu, Manipur, Mizoram and Sikkim indicating the likelihood of outbreaks in La Nina condition.

CONCLUSIONS

The generated map, representing spatial correlation between ' + winter ONI' and 'malaria case index', indicates positive correlations in eastern part, while negative correlations in western part of India. This study provides plausible guidelines to national programme for planning intervention measures in view of ENSO events. For better resolution, district level study with inclusion of IOD and 'epochal variation of monsoon rainfall' factors at micro-level is desired for better forecast of malaria outbreaks in the regions with 'no correlation'.

Behavioral patterns, parity rate and natural infection analysis in anopheline species involved in the transmission of malaria in the northeastern Brazilian Amazon region

The characterization of behavioral patterns allows a better understanding of the transmission dynamics and the design of more effective malaria vector control strategies. This study analyzed the behavioral patterns of the Anopheles species of the Coração district situated in the northeast of the Brazilian Amazon region. The behavioral patterns of the anopheline species were measured based on the 36 collection sites of this district from December 2010 to November 2011. Collections of four hours for three consecutive nights each month and four 12-h collections, comprising two in the rainy season and two in the dry season, were performed. Furthermore, to infer the anthropophily and zoophily indexes, four additional four-hour collections were performed. The samples were also evaluated for parity rate and natural infectivity for Plasmodium spp. A total of 1689 anophelines were captured, comprising of nine species and two subgenera (Nyssorhynchus - six species, and Anopheles - three species). Anopheles darlingi was the most abundant and widely distributed species in the area, followed by A. braziliensis and A.marajoara. Anopheles darlingi and A. marajoara were the only species present in the four collections of 12-h, but only A. darlingi showed activity throughout night. Anopheles darlingi was the most anthropophilic species (AI=0.40), but the zoophily index was higher (ZI=0.60), revealing an eclectic and opportunistic behavior. Of the six most frequent species, A. nuneztovari s.l. was the most zoophilic species (ZI=1.00). All captured species showed predominance towards biting in outdoor environments. Anopheles darlingi and A. braziliensis showed multimodal biting peaks, whereas A. marajoara revealed a stable pattern, with the biting peak after sunset. Using the PCR technique, no anopheline was found infected with the malaria parasite. Since A. darlingi and A. marajoara are recognized as important vectors in this region, the district of Coração may be considered as a highly potent area for transmission of malaria, therefore, the prevention and surveillance measures should be taken constantly to prevent the same. The role of A. braziliensis as malaria vector needs to be urgently investigated.

El Niño-based malaria epidemic warning for Oromia, Ethiopia, from August 2016 to July 2017

OBJECTIVE

Tropical highland malaria intensifies and shifts to higher altitudes during exceptionally warm years. Above-normal temperatures associated with El Niño during boreal winter months (December-March) may intensify malaria in East African highlands. We assessed the malaria risk for Oromia, the largest region of Ethiopia with around 30 million inhabitants.

METHODS

Simple linear regression and spatial analyses were used to associate sea surface temperatures (SST) in the Pacific and surface temperatures in Ethiopia with annual malaria risk in Oromia, based on confirmed cases of malaria between 1982 and 2005.

RESULTS

A strong association (R² = 0.6, P < 0.001) was identified between malaria and sea surface temperatures in the Pacific, anticipating a 70% increase in malaria risk for the period from August 2016 to July 2017. This forecast was quantitatively supported by elevated land surface temperatures (+1.6 °C) in December 2015. When more station data become available and mean March 2016 temperatures from meteorological stations can be taken into account, a more robust prediction can be issued.

CONCLUSION

An epidemic warning is issued for Oromia, Ethiopia, between August 2016 and July 2017 and may include the pre-July short malaria season. Similar relationships reported for Madagascar point to an epidemic risk for all East African highlands with around 150 million people. Preparedness for this high risk period would include pre-emptive intradomestic spraying with insecticides, adequate stocking of antimalarials, and spatial extension of diagnostic capacity and more frequent reporting to enable a rapid public health response when and where required.

Malaria and large dams in sub-Saharan Africa: future impacts in a changing climate

BACKGROUND

Sub-Saharan Africa (SSA) has embarked on a new era of dam building to improve food security and promote economic development. Nonetheless, the future impacts of dams on malaria transmission are poorly understood and seldom investigated in the context of climate and demographic change.

METHODS

The distribution of malaria in the vicinity of 1268 existing dams in SSA was mapped under the Intergovernmental Panel on Climate Change (IPCC) representative concentration pathways (RCP) 2.6 and 8.5. Population projections and malaria incidence estimates were used to compute population at risk of malaria in both RCPs. Assuming no change in socio-economic interventions that may mitigate impacts, the change in malaria stability and malaria burden in the vicinity of the dams was calculated for the two RCPs through to the 2080s. Results were compared against the 2010 baseline. The annual number of malaria cases associated with dams and climate change was determined for each of the RCPs.

RESULTS

The number of dams located in malarious areas is projected to increase in both RCPs. Population growth will add to the risk of transmission. The population at risk of malaria around existing dams and associated reservoirs, is estimated to increase from 15 million in 2010 to 21-23 million in the 2020s, 25-26 million in the 2050s and 28-29 million in the 2080s, depending on RCP. The number of malaria cases associated with dams in malarious areas is expected to increase from 1.1 million in 2010 to 1.2-1.6 million in the 2020s, 2.1-3.0 million in the 2050s and 2.4-3.0 million in the 2080s depending on RCP. The number of cases will always be higher in RCP 8.5 than RCP 2.6.

CONCLUSION

In the absence of changes in other factors that affect transmission (e.g., socio-economic), the impact of dams on malaria in SSA will be significantly exacerbated by climate change and increases in population. Areas without malaria transmission at present, which will transition to regions of unstable transmission, may be worst affected. Modifying conventional water management frameworks to improve malaria control, holds the potential to mitigate some of this increase and should be more actively implemented.

Seasonal and Geographic Variation of Pediatric Malaria in Burundi: 2011 to 2012

We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy seasons (February to May; September to November) and two dry seasons (June to August; December to January). The Getis-Ord Gi* statistic was used to examine seasonal spatial patterns of pediatric malaria, whereas geographically weighted regression (GWR) were used to examine the potential role of environmental variables on the spatial patterns of cases. There were a total of 19,890 pediatric malaria cases reported during the study period. The incidence among males was higher than that among females; and it was higher in rural districts. The seasonal incidence peaks occurred in the northern half of the country during the wet season while during the dry season, incidence was higher in southern Burundi. Elevation played a greater role in explaining variance in the prevalence of pediatric malaria during seasonal peaks than rainfall. The counterintuitive finding in northern Burundi confirms previous findings and suggests other factors (e.g., land cover/land use) facilitate the persistence of the mosquito population in the highlands of Africa.

Fitting the Incidence Data from the City of Campinas, Brazil, Based on Dengue Transmission Modellings Considering Time-Dependent Entomological Parameters

Four time-dependent dengue transmission models are considered in order to fit the incidence data from the City of Campinas, Brazil, recorded from October 1^st 1995 to September 30^th 2012. The entomological parameters are allowed to depend on temperature and precipitation, while the carrying capacity and the hatching of eggs depend only on precipitation. The whole period of incidence of dengue is split into four periods, due to the fact that the model is formulated considering the circulation of only one serotype. Dengue transmission parameters from human to mosquito and mosquito to human are fitted for each one of the periods. The time varying partial and overall effective reproduction numbers are obtained to explain the incidence of dengue provided by the models.

ASSESSING THE EFFECTS OF TEMPERATURE AND DENGUE VIRUS LOAD ON DENGUE TRANSMISSION

In this study, we propose a model to assess the effect of temperature on the incidence of dengue fever. For this, we take into account the dependence of the entomological and epidemiological parameters of the transmitter vector Aedes aegypti with respect to the temperature. The model consists of an ODE system that describes the transmission between humans and mosquitoes considering the aquatic stage of the vector population. The qualitative analysis of the model is made in terms of the parameters [Formula: see text] and [Formula: see text], which represent the basic offspring of mosquitoes, and the basic reproductive number of the disease, respectively. If [Formula: see text] mosquito population extinguishes while for [Formula: see text] it tends asymptotically to a nonzero equilibrium. Analogously, the disease transmission is eliminated if [Formula: see text], and it approaches an endemic equilibrium for [Formula: see text]. Using entomological data of mosquitoes as well as experimental data of disease transmission we evaluate [Formula: see text] and [Formula: see text] at different temperatures, obtaining that around [Formula: see text]C both parameters attain their maximum. Sensitivity analysis reveals that infection rates and mosquito mortality are the parameters for which [Formula: see text] is more sensitive.

Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia

BACKGROUND

The impacts of interannual climate fluctuations on vector-borne diseases, especially malaria, have received considerable attention in the scientific literature. These effects can be significant in semi-arid and high-elevation areas such as the highlands of East Africa because cooler temperature and seasonally dry conditions limit malaria transmission. Many previous studies have examined short-term lagged effects of climate on malaria (weeks to months), but fewer have explored the possibility of longer-term seasonal effects.

METHODS

This study assessed the interannual variability of malaria occurrence from 2001 to 2009 in the Amhara region of Ethiopia. We tested for associations of climate variables summarized during the dry (January-April), early transition (May-June), and wet (July-September) seasons with malaria incidence in the early peak (May-July) and late peak (September-December) epidemic seasons using generalized linear models. Climate variables included land surface temperature (LST), rainfall, actual evapotranspiration (ET), and the enhanced vegetation index (EVI).

RESULTS

We found that both early and late peak malaria incidence had the strongest associations with meteorological conditions in the preceding dry and early transition seasons. Temperature had the strongest influence in the wetter western districts, whereas moisture variables had the strongest influence in the drier eastern districts. We also found a significant correlation between malaria incidence in the early and the subsquent late peak malaria seasons, and the addition of early peak malaria incidence as a predictor substantially improved models of late peak season malaria in both of the study sub-regions.

CONCLUSIONS

These findings suggest that climatic effects on malaria prior to the main rainy season can carry over through the rainy season and affect the probability of malaria epidemics during the late malaria peak. The results also emphasize the value of combining environmental monitoring with epidemiological surveillance to develop forecasts of malaria outbreaks, as well as the need for spatially stratified approaches that reflect the differential effects of climatic variations in the different sub-regions.

Climate Change and Spatiotemporal Distributions of Vector-Borne Diseases in Nepal--A Systematic Synthesis of Literature

Background

Despite its largely mountainous terrain for which this Himalayan country is a popular tourist destination, Nepal is now endemic for five major vector-borne diseases (VBDs), namely malaria, lymphatic filariasis, Japanese encephalitis, visceral leishmaniasis and dengue fever. There is increasing evidence about the impacts of climate change on VBDs especially in tropical highlands and temperate regions. Our aim is to explore whether the observed spatiotemporal distributions of VBDs in Nepal can be related to climate change.

Methodology

A systematic literature search was performed and summarized information on climate change and the spatiotemporal distribution of VBDs in Nepal from the published literature until December2014 following providing items for systematic review and meta-analysis (PRISMA) guidelines.

Principal Findings

We found 12 studies that analysed the trend of climatic data and are relevant for the study of VBDs, 38 studies that dealt with the spatial and temporal distribution of disease vectors and disease transmission. Among 38 studies, only eight studies assessed the association of VBDs with climatic variables. Our review highlights a pronounced warming in the mountains and an expansion of autochthonous cases of VBDs to non-endemic areas including mountain regions (i.e., at least 2,000 m above sea level). Furthermore, significant relationships between climatic variables and VBDs and their vectors are found in short-term studies.

Conclusion

Taking into account the weak health care systems and difficult geographic terrain of Nepal, increasing trade and movements of people, a lack of vector control interventions, observed relationships between climatic variables and VBDs and their vectors and the establishment of relevant disease vectors already at least 2,000 m above sea level, we conclude that climate change can intensify the risk of VBD epidemics in the mountain regions of Nepal if other non-climatic drivers of VBDs remain constant.

Sporogonic Cycles Calculated Using Degree-Days, as a Basis for Comparison of Malaria Parasite Development in Different Eco-Epidemiological Settings in India

In India, malaria transmission is prevalent across diverse geologies and ecologies. Temperature is one of the key determinants of malarial transmission, causing low endemicity in some areas than in others. Using a degree-day model, we estimated the maximum and minimum possible number of days needed to complete a malarial sporogonic cycle (SC), in addition to the possible number of SCs for Plasmodium vivax and Plasmodium falciparum under two different ecological settings with either low or high endemicity for malaria at different elevations. In Raikhalkhatta (in the Himalayan foothills) SCs were modeled as not occurring from November to February, whereas in Gandhonia village (forested hills), all but only one month were suitable for malarial SCs. A minimum of 6 days and maximum of 46 days were required for completion of one SC. Forested hilly areas were more suitable for malaria parasite development in terms of SCs (25 versus 21 for P. falciparum and 32 versus 27 for P. vivax). Degree-days also provided a climatic explanation for the current transmission of malaria at different elevations. The calculation of degree-days and possible SC has applications in the regional analysis of transmission dynamics and management of malaria in view of climate change.

The association of weather variability and under five malaria mortality in KEMRI/CDC HDSS in Western Kenya 2003 to 2008: a time series analysis

Malaria is among the leading causes of mortality in the younger under-five group of children zero to four years of age. This study aims at describing the relationship between rainfall and temperature on under-five malaria or anaemia mortality in Kenya Medical Research Institute and United States Centers for Disease Control (KEMRI/CDC) Health and Demographic Surveillance System (HDSS). This study was conducted through the ongoing KEMRI and CDC collaboration. A general additive model with a Poisson link function was fit to model the weekly association of lagged cumulative rainfall and average temperature on malaria/anemia mortality in KEMRI/CDC HDSS for the period 2003 to 2008. A trend function was included in the model to control for time trends and seasonality not explained by weather fluctuations. 95% confidence intervals was presented with estimates. Malaria or anemia mortality was found to be associated with changes in temperature and rainfall in the KEMRI HDSS, with a delay up to 16 weeks. The empirical estimates of associations describe established biological relationships well. This information, and particularly, the strength of the relationships over longer lead times can highlight the possibility of developing a predictive forecast with lead times up to 16 weeks in order to enhance preparedness to high transmission episodes.

Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk

Around $1.6 billion per year is spent financing anti-malaria initiatives, and though malaria morbidity is falling, the impact of annual epidemics remains significant. Whilst malaria risk may increase with climate change, projections are highly uncertain and to sidestep this intractable uncertainty, adaptation efforts should improve societal ability to anticipate and mitigate individual events. Anticipation of climate-related events is made possible by seasonal climate forecasting, from which warnings of anomalous seasonal average temperature and rainfall, months in advance are possible. Seasonal climate hindcasts have been used to drive climate-based models for malaria, showing significant skill for observed malaria incidence. However, the relationship between seasonal average climate and malaria risk remains unquantified. Here we explore this relationship, using a dynamic weather-driven malaria model. We also quantify key uncertainty in the malaria model, by introducing variability in one of the first order uncertainties in model formulation. Results are visualized as location-specific impact surfaces: easily integrated with ensemble seasonal climate forecasts, and intuitively communicating quantified uncertainty. Methods are demonstrated for two epidemic regions, and are not limited to malaria modeling; the visualization method could be applied to any climate impact.

Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence

Dengue fever is one of the most widespread vector-borne diseases and has caused more than 50 million infections annually over the world. For the purposes of disease prevention and climate change health impact assessment, it is crucial to understand the weather-disease associations for dengue fever. This study investigated the nonlinear delayed impact of meteorological conditions on the spatiotemporal variations of dengue fever in southern Taiwan during 1998-2011. We present a novel integration of a distributed lag nonlinear model and Markov random fields to assess the nonlinear lagged effects of weather variables on temporal dynamics of dengue fever and to account for the geographical heterogeneity. This study identified the most significant meteorological measures to dengue fever variations, i.e., weekly minimum temperature, and the weekly maximum 24-hour rainfall, by obtaining the relative risk (RR) with respect to disease counts and a continuous 20-week lagged time. Results show that RR increased as minimum temperature increased, especially for the lagged period 5-18 weeks, and also suggest that the time to high disease risks can be decreased. Once the occurrence of maximum 24-hour rainfall is >50 mm, an associated increased RR lasted for up to 15 weeks. A temporary one-month decrease in the RR of dengue fever is noted following the extreme rain. In addition, the elevated incidence risk is identified in highly populated areas. Our results highlight the high nonlinearity of temporal lagged effects and magnitudes of temperature and rainfall on dengue fever epidemics. The results can be a practical reference for the early warning of dengue fever.