Modeling the influence of local environmental factors on malaria transmission in Benin and its implications for cohort study

Malaria remains endemic in tropical areas, especially in Africa. For the evaluation of new tools and to further our understanding of host-parasite interactions, knowing the environmental risk of transmission--even at a very local scale--is essential. The aim of this study was to assess how malaria transmission is influenced and can be predicted by local climatic and environmental factors.As the entomological part of a cohort study of 650 newborn babies in nine villages in the Tori Bossito district of Southern Benin between June 2007 and February 2010, human landing catches were performed to assess the density of malaria vectors and transmission intensity. Climatic factors as well as household characteristics were recorded throughout the study. Statistical correlations between Anopheles density and environmental and climatic factors were tested using a three-level Poisson mixed regression model. The results showed both temporal variations in vector density (related to season and rainfall), and spatial variations at the level of both village and house. These spatial variations could be largely explained by factors associated with the house's immediate surroundings, namely soil type, vegetation index and the proximity of a watercourse. Based on these results, a predictive regression model was developed using a leave-one-out method, to predict the spatiotemporal variability of malaria transmission in the nine villages.This study points up the importance of local environmental factors in malaria transmission and describes a model to predict the transmission risk of individual children, based on environmental and behavioral characteristics.     

Ranking Malaria Risk Factors to Guide Malaria Control Efforts in African Highlands

Introduction

Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies.

Methods and Findings

A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through “classification and regression trees”, an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density.

Conclusions

In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

Introduction

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases.

Methods

Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years.

Results

The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series.

Conclusions

G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.     

Focused Screening and Treatment (FSAT): A PCR-Based Strategy to Detect Malaria Parasite Carriers and Contain Drug Resistant P. falciparum, Pailin, Cambodia

Recent studies have shown that Plasmodium falciparum malaria parasites in Pailin province, along the border between Thailand and Cambodia, have become resistant to artemisinin derivatives. To better define the epidemiology of P. falciparum populations and to assess the risk of the possible spread of these parasites outside Pailin, a new epidemiological tool named “Focused Screening and Treatment” (FSAT), based on active molecular detection of asymptomatic parasite carriers was introduced in 2010. Cross-sectional malariometric surveys using PCR were carried out in 20 out of 109 villages in Pailin province. Individuals detected as P. falciparum carriers were treated with atovaquone-proguanil combination plus a single dose of primaquine if the patient was non-G6PD deficient. Interviews were conducted to elicit history of cross-border travel that might contribute to the spread of artemisinin-resistant parasites. After directly observed treatment, patients were followed up and re-examined on day 7 and day 28. Among 6931 individuals screened, prevalence of P. falciparum carriers was less than 1%, of whom 96% were asymptomatic. Only 1.6% of the individuals had a travel history or plans to go outside Cambodia, with none of those tested being positive for P. falciparum. Retrospective analysis, using 2010 routine surveillance data, showed significant differences in the prevalence of asymptomatic carriers discovered by FSAT between villages classified as “high risk” and “low risk” based on malaria incidence data. All positive individuals treated and followed-up until day 28 were cured. No mutant-type allele related to atovaquone resistance was found. FSAT is a potentially useful tool to detect, treat and track clusters of asymptomatic carriers of P. falciparum along with providing valuable epidemiological information regarding cross-border movements of potential malaria parasite carriers and parasite gene flow.     

Longitudinal follow‐up of malaria transmission dynamics in two villages in a Sahelian area of Niger during a nationwide insecticide‐treated bednet distribution programme

Malaria transmission was monitored in two villages in the Sahel zone of Niger over 4 years. During this period, a nationwide vector control programme was carried out in which insecticide‐treated bednets were distributed free to mothers of children aged <5 years. Anopheles gambiae and Anopheles arabiensis (Diptera: Culicidae) were found to be the major malaria vectors. The dynamics of An. gambiae s.l. did not vary dramatically over the study period although the proportion of female mosquitoes found resting indoors decreased in both villages and, in one village, the parity rate and sporozoite index were significantly reduced after bednet distribution. By contrast with An. gambiae, the dynamics of Anopheles funestus altered greatly after the bednet distribution period, when adult density, endophagous rate and sporozoite rates decreased dramatically. Our observations highlight the importance of quantifying and monitoring the dynamics and infections of malaria vectors during large‐scale vector control interventions.     

Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

Background

After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions.

Methods

We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi.

Results

Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk.

Conclusions

The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.     

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America,under Climate Change

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector’s climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: “stabilization” and “high increase”. Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region.     

The campaign against malaria in central western Madagascar: comparison of the efficacy of lambda-cyhalothrin and DDT house spraying. I--Entomological study]

For malaria vector control in Madagascar, the efficacy of lambda-cyhalothrin 10% wettable powder (ICON 10 WP) was compared with DDT 75% WP for house-spraying. This evaluation was conducted from November 1997 to September 1998 in highland villages of Vakinankaratra Region, at the fringe of the malaria epidemic zone, outside the zone covered by routine DDT house-spraying (Opération de pulvérisation intro-domiciliaire de DDT: OPID zone). Treatments were compared by house-spraying in four areas: 1) application of DDT 2g ai/m2 and 2) lambda-cyhalothrin 30 mg ai/m2 in previously unsprayed villages; 3) no intervention (control); 4) OPID 5th cycle of DDT 2g ai/m2. The prevalent vector Anopheles funestus almost disappeared from both the DDT and ICON sprayed areas, whereas in the unsprayed (control) area An. funeslus density went up to 60 females per room in April and there were two seasonal peaks of malaria transmission in January and March (see following paper). In the area sprayed with ICON, the parous rate of An. funestus decreased from 47% pre-spray to 39% six months post-spraying, while the parous rate increased in DDT-sprayed area (from 57% pre-spray to 64% six months post-spray). Bioassays of An. funestus on treated walls, six months post-spray, gave mortality rates of 100% on DDT and 90% on ICON. Conversely, ICON appeared to be more effective than DDT on thatched roofs (66% versus 100%, respectively, six months post-spray). In areas sprayed with DDT or ICON the density of An. arabiensis were little affected. This study demonstrated that, under equivalent conditions, both DDT and lambda-cyhalothrin were effective in reducing malaria transmission on the western fringes of the malaria epidemic zone of the malagasy highlands, with a residual effect lasting at least for six months. Lambda-cyhalothrin appeared to be more effective than DDT in reducing the longevity of malaria vectors. In addition to efficacy, the choice of insecticide for malaria vector control should take into account their acceptability by human populations and their toxicity and persistence in the environment.     

Joint malaria surveys lead towards improved cross-border cooperation between Savannakhet province, Laos and Quang Tri province, Vietnam

ABSTRACT: BACKGROUND: In Savannakhet province, Laos and Quang Tri province, Vietnam, malaria is still an important health problem and most cases are found in the mountainous, forested border areas where ethnic minority groups live. The objectives of this study were to obtain a better joint understanding of the malaria situation along the border and, on the basis of that, improve malaria control methods through better cooperation between the two countries. METHODS: Fourteen villages in Savannakhet and 22 villages in Quang Tri were randomly selected within 5 km from the border where a blood survey for microscopic diagnosis (n = 1256 and n = 1803, respectively), household interviews (n = 400, both sides) and vector surveys were conducted between August and October 2010. Satellite images were used to examine the forest density around the study villages. RESULTS: Malaria prevalence was significantly higher in Laos (5.2%) than in Vietnam (1.8%) and many other differences were found over the short distance across the border. Bed net coverage was high (> 90%) in both Laos and Vietnam but, while in Laos more than 60% of the nets were long-lasting insecticide-treated, Vietnam used indoor residual spraying in this area and the nets were untreated. Anopheles mosquitoes were more abundant in Laos than in Vietnam, especially many Anopheles dirus were captured in indoor light traps while none were collected in Vietnam. The forest cover was higher around the Lao than the Vietnamese villages. After this study routine exchange of malaria surveillance data was institutionalized and for the first time indoor residual spraying was applied in some Lao villages. CONCLUSIONS: The abundance of indoor-collected An. dirus on the Laos side raises doubts about the effectiveness of a sole reliance on long-lasting insecticide-treated net in this area. Next to strengthening the early detection, correct diagnosis and prompt, adequate treatment of malaria infections, it is recommended to test focal indoor residual spraying and the promotion of insect repellent use in the early evening as additional vector interventions. Conducting joint malaria surveys by staff of two countries proved to be effective in stimulating better collaboration and improve cross-border malaria control.     

Dry season determinants of malaria disease and net use in Benin, West Africa

Background

To achieve malaria eradication, control efforts have to be sustained even when the incidence of malaria cases becomes low during the dry season. In this work, malaria incidence and its determinants including bed net use were investigated in children of under 5 years of age in 28 villages in southern Benin during the dry season.

Methods and Findings

Mean malaria clinical incidence was measured in children aged 0–5 years by active case detection in 28 villages of the Ouidah-Kpomasse-Tori Bossito sanitary district between November 2007 and March 2008. Using Poisson mixed-effect models, malaria incidence was assessed according to the level of transmission by different vector species, and Long-Lasting Insecticide-treated mosquito Nets (LLIN) use and ownership. Then, a Binomial mixed-effect model was developed to assess whether nighttime temperature (derived from MODIS remote sensing data), biting nuisance and LLIN ownership are good predictors of LLIN use >60%. Results suggested that Anopheles funestus (Incidence Rates Ratio (IRR) = 3.38 [IC95 1.91–6]) rather than An. gambiae s.s. is responsible for malaria transmission. A rate of LLIN use >60% was associated with a lower risk of malaria (IRR = 0.6 [IC95 0.37–0.99]). Low nocturnal temperature and high biting nuisance were good predictors of LLIN use >60%.

Conclusions

As recommended by the Malaria Eradication (MalERA) Consultative Group on Modelling, there is a need to understand better the effects of seasonality on malaria morbidity. This study highlights the need to take into account the specificity of malaria epidemiology during the dry-hot season and get a better understanding of the factors that influence malaria incidence and net use. These findings should help National Malaria Control Programmes to implement more effective and sustainable malaria control strategies in Africa.     

Abundance and distribution of Anopheles mosquitoes in a malaria endemic area along the Thai‐Lao border

Malaria is an important public health problem in Thailand, especially along international borders. In this study, we conducted a longitudinal entomological survey in six villages and rubber plantation sites to address the spatio‐temporal abundance and behavior of malaria vectors in Ubon Ratchathani Province along the Thailand‐Laos border. Adult female mosquitoes were collected by human landing collections (indoor and outdoor) and by cattle bait collections twice per year, during rainy and dry seasons. Mosquitoes were morphologically identified and sibling species were determined by allele‐specific PCR. Of the 10,024 Anopheles, 9,328 (93.1%) and 696 (6.9%) were collected during the rainy and dry seasons, respectively. A total of 9,769 (97.5%) and 255 (2.5%) was collected on cattle and human baits, respectively. Very few primary and secondary malaria vectors were collected, consisting of 12 specimens of An. dirus, eight An. minimus, and seven An. aconitus. Of the 152 specimens of the Maculatus Group, only three were identified to An sawadwongporni by molecular methods. The others were 112 An. rampae, a non‐vector, that were not amplified or were misidentified as other non‐vectors. The very low density of primary malaria vectors found in the study villages suggests that entomological risk and malaria transmission is higher in neighboring forest areas. Further studies on malaria vector distribution, as well as human behaviors, are needed to understand malaria transmission dynamics in the province and to develop suitable vector control methods.     

Evaluating the efficacy of biological and conventional insecticides with the new ‘MCD bottle’ bioassay

Background

Urban malaria is considered to be one of the most significant infectious diseases due to varied socioeconomic problems especially in tropical countries like India. Among the south Indian cities, Chennai is endemic for malaria. The present study aimed to identify the hot spots of malaria prevalence and the relationship with other factors in Chennai during 2005-2011.

Methods

Data on zone-wise and ward-wise monthly malaria positive cases were collected from the Vector Control Office, Chennai Corporation, for the year 2005 to 2011 and verified using field data. This data was used to calculate the prevalence among thousand people. Hotspot analysis for all the years in the study period was done to observe the spatial trend. Association of environmental factors like altitude, population density and climatic variables was assessed using ArcGIS 9.3 version and SPSS 11.5. Pearson’s correlation of climate parameters at 95% and 99% was considered to be the most significant. Social parameters of the highly malaria prone region were evaluated through a structured random questionnaire field survey.

Results

Among the ten zones of Chennai Corporation, Basin Bridge zone showed high malaria prevalence during the study period. The ‘hotspot’ analysis of malaria prevalence showed the emergence of newer hotspots in the Adyar zone. These hotspots of high prevalence are places of moderately populated and moderately elevated areas. The prevalence of malaria in Chennai could be due to rainfall and temperature, as there is a significant correlation with monthly rainfall and one month lag of monthly mean temperature. Further it has been observed that the socioeconomic status of people in the malaria hotspot regions and unhygienic living conditions were likely to aggravate the malaria problem.

Conclusion

Malaria hotspots will be the best method to use for targeting malaria control activities. Proper awareness and periodical monitoring of malaria is one of the quintessential steps to control this infectious disease. It has been argued that identifying the key environmental conditions favourable for the occurrence and spread of malaria must be integrated and documented to aid future predictions of malaria in Chennai.     

A longitudinal entomologic survey on the transmission of malaria in Ouagadougou (Burkina Faso)

A longitudinal entomological malaria survey was carried out in five zones of the town of Ouagadougou, Burkina Faso, and in three neighbouring villages. The main vector is Anopheles gambiae s.l. with An. funestus having a role in some localities during the dry season. Pyrethrum spray catches were carried out once or twice per month to determine variations in vector density. Inoculation rates were estimated from the number of blood-fed vectors per man and from the sporozoite rates. Larval sampling was routinely carried out all over the urban area in order to map the larval breeding sites. Widely different degrees of malaria transmission were documented in the urban area mainly related to the spatial and temporal distribution of An. gambiae larval breeding sites. Higher inoculation rates, depending both on higher vector densities and sporozoite rates, were documented in the villages.     

Comparing models for early warning systems of neglected tropical diseases

Background

Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.

Methodology/Principal Findings

In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R ² for forecasts of “out-of-fit” data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.

Conclusions/Significance

This study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of “out-of-fit” data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion.     

Landscape Movements of Anopheles gambiae Malaria Vector Mosquitoes in Rural Gambia

Background

For malaria control in Africa it is crucial to characterise the dispersal of its most efficient vector, Anopheles gambiae, in order to target interventions and assess their impact spatially. Our study is, we believe, the first to present a statistical model of dispersal probability against distance from breeding habitat to human settlements for this important disease vector.

Methods/Principal Findings

We undertook post-hoc analyses of mosquito catches made in The Gambia to derive statistical dispersal functions for An. gambiae sensu lato collected in 48 villages at varying distances to alluvial larval habitat along the River Gambia. The proportion dispersing declined exponentially with distance, and we estimated that 90% of movements were within 1.7 km. Although a ‘heavy-tailed’ distribution is considered biologically more plausible due to active dispersal by mosquitoes seeking blood meals, there was no statistical basis for choosing it over a negative exponential distribution. Using a simple random walk model with daily survival and movements previously recorded in Burkina Faso, we were able to reproduce the dispersal probabilities observed in The Gambia.

Conclusions/Significance

Our results provide an important quantification of the probability of An. gambiae s.l. dispersal in a rural African setting typical of many parts of the continent. However, dispersal will be landscape specific and in order to generalise to other spatial configurations of habitat and hosts it will be necessary to produce tractable models of mosquito movements for operational use. We show that simple random walk models have potential. Consequently, there is a pressing need for new empirical studies of An. gambiae survival and movements in different settings to drive this development.     

On the generalized poisson regression mixture model for mapping quantitative trait loci with count data

Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean-variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.     

The Role of Climate Variability in the Spread of Malaria in Bangladeshi Highlands

Background

Malaria is a major public health problem in Bangladesh, frequently occurring as epidemics since the 1990s. Many factors affect increases in malaria cases, including changes in land use, drug resistance, malaria control programs, socioeconomic issues, and climatic factors. No study has examined the relationship between malaria epidemics and climatic factors in Bangladesh. Here, we investigate the relationship between climatic parameters [rainfall, temperature, humidity, sea surface temperature (SST), El Niño-Southern Oscillation (ENSO), the normalized difference vegetation index (NDVI)], and malaria cases over the last 20 years in the malaria endemic district of Chittagong Hill Tracts (CHT).

Methods and Principal Findings

Monthly malaria case data from January 1989 to December 2008, monthly rainfall, temperature, humidity sea surface temperature in the Bay of Bengal and ENSO index at the Niño Region 3 (NIÑO3) were used. A generalized linear negative binomial regression model was developed using the number of monthly malaria cases and each of the climatic parameters. After adjusting for potential mutual confounding between climatic factors there was no evidence for any association between the number of malaria cases and temperature, rainfall and humidity. Only a low NDVI was associated with an increase in the number of malaria cases. There was no evidence of an association between malaria cases and SST in the Bay of Bengal and NIÑO3.

Conclusion and Significance

It seems counterintuitive that a low NDVI, an indicator of low vegetation greenness, is associated with increases in malaria cases, since the primary vectors in Bangladesh, such as An. dirus, are associated with forests. This relationship can be explained by the drying up of rivers and streams creating suitable breeding sites for the vector fauna. Bangladesh has very high vector species diversity and vectors suited to these habitats may be responsible for the observed results.     

Shifts in malaria vector species composition and transmission dynamics along the Kenyan coast over the past 20 years

Background

Over the past 20 years, numerous studies have investigated the ecology and behaviour of malaria vectors and Plasmodium falciparum malaria transmission on the coast of Kenya. Substantial progress has been made to control vector populations and reduce high malaria prevalence and severe disease. The goal of this paper was to examine trends over the past 20 years in Anopheles species composition, density, blood-feeding behaviour, and P. falciparum sporozoite transmission along the coast of Kenya.

Methods

Using data collected from 1990 to 2010, vector density, species composition, blood-feeding patterns, and malaria transmission intensity was examined along the Kenyan coast. Mosquitoes were identified to species, based on morphological characteristics and DNA extracted from Anopheles gambiae for amplification. Using negative binomial generalized estimating equations, mosquito abundance over the period were modelled while adjusting for season. A multiple logistic regression model was used to analyse the sporozoite rates.

Results

Results show that in some areas along the Kenyan coast, Anopheles arabiensis and Anopheles merus have replaced An. gambiae sensu stricto (s.s.) and Anopheles funestus as the major mosquito species. Further, there has been a shift from human to animal feeding for both An. gambiae sensu lato (s.l.) (99% to 16%) and An. funestus (100% to 3%), and P. falciparum sporozoite rates have significantly declined over the last 20 years, with the lowest sporozoite rates being observed in 2007 (0.19%) and 2008 (0.34%). There has been, on average, a significant reduction in the abundance of An. gambiae s.l. over the years (IRR?=?0.94, 95% CI 0.90–0.98), with the density standing at low levels of an average 0.006 mosquitoes/house in the year 2010.

Conclusion

Reductions in the densities of the major malaria vectors and a shift from human to animal feeding have contributed to the decreased burden of malaria along the Kenyan coast. Vector species composition remains heterogeneous but in many areas An. arabiensis has replaced An. gambiae as the major malaria vector. This has important implications for malaria epidemiology and control given that this vector predominately rests and feeds on humans outdoors. Strategies for vector control need to continue focusing on tools for protecting residents inside houses but additionally employ outdoor control tools because these are essential for further reducing the levels of malaria transmission.     

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District,South Africa

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000 persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5–20 km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10 km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ < 0.05) and yielded a deviance (D²) of 36%. The significantly positive relationship (ρ < 0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a₁ index) and malaria distribution in buffer distances of 0.5, 10, and 20 km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10 km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.