Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands

Geospatial based model for malaria risk prediction in Kilombero valley, South-eastern, Tanzania

BACKGROUND

Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

METHODS

This study employs a geospatial based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability.

RESULTS

The study demonstrates that the majority of the study area falls under moderate risk level (61%), followed by the low risk level (31%), while the high malaria risk area covers a small area, which occupies only 8% of the total area.

CONCLUSION

The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions.

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.

Impact of aerial humidity on seasonal malaria: an ecological study in Zambia

Background

Seasonal patterns of malaria cases in many parts of Africa are generally associated with rainfall, yet in the dry seasons, malaria transmission declines but does not always cease. It is important to understand what conditions support these periodic cases. Aerial moisture is thought to be important for mosquito survival and ability to forage, but its role during the dry seasons has not been well studied. During the dry season aerial moisture is minimal, but intermittent periods may arise from the transpiration of peri-domestic trees or from some other sources in the environment. These periods may provide conditions to sustain pockets of mosquitoes that become active and forage, thereby transmitting malaria. In this work, humidity along with other ecological variables that may impact malaria transmission have been examined.

Methods

Negative binomial regression models were used to explore the association between peri-domestic tree humidity and local malaria incidence. This was done using sensitive temperature and humidity loggers in the rural Southern Province of Zambia over three consecutive years. Additional variables including rainfall, temperature and elevation were also explored.

Results

A negative binomial model with no lag was found to best fit the malaria cases for the full year in the evaluated sites of the Southern Province of Zambia. Local tree and granary night-time humidity and temperature were found to be associated with local health centre-reported incidence of malaria, while rainfall and elevation did not significantly contribute to this model. A no lag and one week lag model for the dry season alone also showed a significant effect of humidity, but not temperature, elevation, or rainfall.

Conclusion

The study has shown that throughout the dry season, periodic conditions of sustained humidity occur that may permit foraging by resting mosquitoes, and these periods are associated with increased incidence of malaria cases. These results shed a light on conditions that impact the survival of the common malaria vector species, Anopheles arabiensis, in arid seasons and suggests how they emerge to forage when conditions permit.

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

BACKGROUND

Persons with submicroscopic malaria infection are a major reservoir of gametocytes that sustain malaria transmission in sub-Saharan Africa. Despite recent decreases in the national malaria burden in Kenya due to vector control interventions, malaria transmission continues to be high in western regions of the country bordering Lake Victoria. The objective of this study was to advance knowledge of the topographical, demographic and behavioral risk factors associated with submicroscopic malaria infection in the Lake Victoria basin in Kisumu County.

METHODS

Cross-sectional community surveys for malaria infection were undertaken in three eco-epidemiologically distinct zones in Nyakach sub-County, Kisumu. Adjacent regions were topologically characterized as lakeshore, hillside and highland plateau. Surveys were conducted during the 2019 and 2020 wet and dry seasons. Finger prick blood smears and dry blood spots (DBS) on filter paper were collected from 1,777 healthy volunteers for microscopic inspection and real time-PCR (RT-PCR) diagnosis of Plasmodium infection. Persons who were PCR positive but blood smear negative were considered to harbor submicroscopic infections. Topographical, demographic and behavioral risk factors were correlated with community prevalence of submicroscopic infections.

RESULTS

Out of a total of 1,777 blood samples collected, 14.2% (253/1,777) were diagnosed as submicroscopic infections. Blood smear microscopy and RT-PCR, respectively, detected 3.7% (66/1,777) and 18% (319/1,777) infections. Blood smears results were exclusively positive for P. falciparum, whereas RT-PCR also detected P. malariae and P. ovale mono- and co-infections. Submicroscopic infection prevalence was associated with topographical variation (χ2 = 39.344, df = 2, p<0.0001). The highest prevalence was observed in the lakeshore zone (20.6%, n = 622) followed by the hillside (13.6%, n = 595) and highland plateau zones (7.9%, n = 560). Infection prevalence varied significantly according to season (χ2 = 17.374, df = 3, p<0.0001). The highest prevalence was observed in residents of the lakeshore zone in the 2019 dry season (29.9%, n = 167) and 2020 and 2019 rainy seasons (21.5%, n = 144 and 18.1%, n = 155, respectively). In both the rainy and dry seasons the likelihood of submicroscopic infection was higher in the lakeshore (AOR: 2.71, 95% CI = 1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI = 1.17-2.61, p = 0.007) than in the highland plateau zones. Residence in the lakeshore zone (p<0.0001), male sex (p = 0.025), school age (p = 0.002), and living in mud houses (p = 0.044) increased the risk of submicroscopic malaria infection. Bed net use (p = 0.112) and occupation (p = 0.116) were not associated with submicroscopic infection prevalence.

CONCLUSION

Topographic features of the local landscape and seasonality are major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya. Diagnostic tests more sensitive than blood smear microscopy will allow for monitoring and targeting geographic sites where additional vector interventions are needed to reduce malaria transmission.

Differences in malaria and haematocrit presentation in children living in different settings, North West Region, Cameroon

BACKGROUND

Malaria continues to be a major cause of morbidity and mortality in Cameroon. With all efforts being made to eliminate malaria, it is imperative to describe the epidemiology of the disease in different parts of the country in order to inform control policies. This study aimed to present the differences in the prevalence and intensity of malaria and the anaemic status of children living in different areas of the North West region of Cameroon.

MATERIALS AND METHODS

This study was carried out from April 2016-July 2017. Blood samples were collected from children via finger pricking. Stained thick and thin blood films were examined through microscopy (x100) to detect the presence of parasites and to estimate the geometric mean parasite density (GMPD). Packed cell volume (PCV) values were determined by micro-centrifugation. Data was analysed using SPSS to determine proportions and test for significance levels between these.

RESULTS

Overall prevalence of malaria was 45.3%. Awing and Obang recorded the highest prevalence while Mankon and Nkwen recorded the lowest (p=0.01). The GMPD of infection was highly heterogeneous between the different localities (p=0.03). Age significantly affected the prevalence of malaria (p=0.02). Sex did not affect the prevalence nor the GMPD of malaria infection (p>0.05). Overall mean PCV value was 32.9±3.9. Localities in urban settings recorded the highest mean PCV values compared to those in rural settings (p=0.68). Sex and age did not affect mean PCV values (p>0.05).

CONCLUSION

Malaria still remains a major problem in the North West region of Cameroon. Malaria control interventions should therefore be based on evident spatial and temporal heterogeneity of Plasmodium species in a particular area so as not to waste resources that would only be of limited effectiveness and value to the populations at risk.

Evaluating the Effects of Climate and Environmental Factors on Under-5 Children Malaria Spatial Distribution Using Generalized Additive Models (GAMs)

Although malaria burden has declined globally following scale up of intervention, the disease has remained a leading cause of hospitalization and deaths among children aged under-5 years in Nigeria. Malaria is known to be related to climate and environmental conditions. Previous research has usually studied the effects of these factors, neglecting possible correlation between them, high correlation among variables is a source of multicollinearity that induces overfitting in regression modelling. In this paper, a factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria. The GAM incorporated the complexity of the survey data, while simultaneously modelling the nonlinear and spatial random effects to allow a more precise identification of the major malaria risk factors that influence the geographical distribution of the disease. From our findings, the three latent factor components (constituted by humidity, precipitation, potential evapotranspiration, and wet days/maximum and minimum temperature/proximity to permanent waters, respectively) were significantly associated with malaria prevalence. Our analysis also detected statistically significant and nonlinear effect of altitude: the risk of malaria increased with lower values but declined sharply with higher values. A significant spatial variability in under-5 malaria prevalence across the survey clusters was also observed; malaria burden was higher in the northern part of Nigeria. Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015–2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

Quantifying Seasonal Variation in Insecticide-Treated Net Use among Those with Access

Seasonal variation in the proportion of the population using an insecticide-treated net (ITN) is well documented and is widely believed to be dependent on mosquito abundance and heat, driven by rainfall and temperature. However, seasonal variation in ITN use has not been quantified controlling for ITN access. Demographic and Health Survey and Malaria Indicator Survey datasets, their georeferenced data, and public rainfall and climate layers were pooled for 21 countries. Nine rainfall typologies were developed from rainfall patterns in Köppen climate zones. For each typology, the odds of ITN use among individuals with access to an ITN within their households (“ITN use given access”) were estimated for each month of the year, controlling for region, wealth quintile, residence, year, temperature, and malaria parasitemia level. Seasonality of ITN use given access was observed over all nine rainfall typologies and was most pronounced in arid climates and less pronounced where rainfall was relatively constant throughout the year. Peak ITN use occurred 1–3 months after peak rainfall and corresponded with peak malaria incidence and average malaria transmission season. The observed lags between peak rainfall and peak ITN use given access suggest that net use is triggered by mosquito density. In equatorial areas, ITN use is likely to be high year-round, given the presence of mosquitoes and an associated year-round perceived malaria risk. These results can be used to inform behavior change interventions to improve ITN use in specific times of the year and to inform geospatial models of the impact of ITNs on transmission.

A Malaria Transmission Model Predicts Holoendemic, Hyperendemic, and Hypoendemic Transmission Patterns Under Varied Seasonal Vector Dynamics

A model is developed of malaria (Plasmodium falciparum) transmission in vector (Anopheles gambiae) and human populations that include the capacity for both clinical and parasite suppressing immunity. This model is coupled with a population model for Anopheles gambiae that varies seasonal with temperature and larval habitat availability. At steady state, the model clearly distinguishes uns hypoendemic transmission patterns from stable hyperendemic and holoendemic patterns of transmission. The model further distinguishes hyperendemic from holoendemic disease based on seasonality of infection. For hyperendemic and holoendemic transmission, the model produces the relationship between entomological inoculation rate and disease prevalence observed in the field. It further produces expected rates of immunity and prevalence across all three endemic patterns. The model does not produce mesoendemic transmission patterns at steady state for any parameter choices, leading to the conclusion that mesoendemic patterns occur during transient states or as a result of factors not included in this study. The model shows that coupling the effect of varying larval habitat availability with the effects of clinical and parasite-suppressing immunity is enough to produce known patterns of malaria transmission.

High-accuracy detection of malaria vector larval habitats using drone-based multispectral imagery

Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.

The most efficient malaria vector in the Latin American region is Nyssorhynchus darlingi (formerly Anopheles darlingi). In Amazonian Peru, where malaria is endemic, Ny. darlingi feeds both indoors and outdoors (endophagy, exophagy), depending on the local environment, and rests outdoors (exophily). LLINs and IRS, the most common tools employed for vector control, target endophagic and endophilic mosquitoes. Thus, they are only partially effective against Ny. darlingi. Control of the aquatic stages of vector mosquitoes, larval source management (LSM), targets the most productive breeding sites nearest to human habitation. In four riverine communities, we used drones with high-resolution imagery as a key initial step to analyze water bodies within the estimated flight range of Ny. darlingi, ~ 1 km. We found distinctive spectral profiles for water bodies that were positive versus negative for Ny. darlingi. The methodology and analysis reported here provide the basis for testing whether LSM can be combined successfully with LLINs and IRS to contribute to the elimination of transmission in malaria hotspots in the Amazon.

Topographic mapping of the interfaces between human and aquatic mosquito habitats to enable barrier targeting of interventions against malaria vectors

Geophysical topographic metrics of local water accumulation potential are freely available and have long been known as high-resolution predictors of where aquatic habitats for immature Anopheles mosquitoes are most abundant, resulting in elevated densities of adult malaria vectors and human infection burden. Using existing entomological and epidemiological survey data, here we illustrate how topography can also be used to map out the interfaces between wet, unoccupied valleys and dry, densely populated uplands, where malaria vector densities and infection risk are focally exacerbated. These topographically identifiable geophysical boundaries experience disproportionately high vector densities and malaria transmission risk, because this is where Anopheles mosquitoes first encounter humans when they search for blood after emerging or ovipositing in the valleys. Geophysical topographic indicators accounted for 67% of variance for vector density but for only 43% for infection prevalence, so they could enable very selective targeting of interventions against the former but not the latter (targeting ratios of 5.7 versus 1.5 to 1, respectively). So, in addition to being useful for targeting larval source management to wet valleys, geophysical topographic indicators may also be used to selectively target adult Anopheles mosquitoes with insecticidal residual sprays, fencing, vapour emanators or space sprays to barrier areas along their fringes.

Dynamic malaria hotspots in an open cohort in western Kenya

Malaria hotspots, defined as areas where transmission intensity exceeds the average level, become more pronounced as transmission declines. Targeting hotspots may accelerate reductions in transmission and could be pivotal for malaria elimination. Determinants of hotspot location, particularly of their movement, are poorly understood. We used spatial statistical methods to identify foci of incidence of self-reported malaria in a large census population of 64,000 people, in 8,290 compounds over a 2.5-year study period. Regression models examine stability of hotspots and identify static and dynamic correlates with their location. Hotspot location changed over short time-periods, rarely recurring in the same area. Hotspots identified in spring versus fall season differed in their stability. Households located in a hotspot in the fall were more likely to be located in a hotspot the following fall (RR = 1.77, 95% CI: 1.66-1.89), but the opposite was true for compounds in spring hotspots (RR = 0.15, 95% CI: 0.08-0.28). Location within a hotspot was related to environmental and static household characteristics such as distance to roads or rivers. Human migration into a household was correlated with risk of hotspot membership, but the direction of the association differed based on the origin of the migration event.

Impact of iron fortification on the geospatial patterns of malaria and non-malaria infection risk among young children: a secondary spatial analysis of clinical trial data from Ghana

OBJECTIVES

Patterns of infection among children with varying levels of iron status in a malaria endemic area may vary spatially in ways requiring integrated infection and iron deficiency control programmes. The objective of this secondary analysis was to determine the geospatial factors associated with malaria and non-malaria infection status among young Ghanaian children at the end of a 5-month iron intervention trial.

DESIGN

Cluster-randomised controlled trial.

SETTING

Rural Ghana PARTICIPANTS: 1943 children (6-35 months of age) with geocoded compounds.

INTERVENTIONS

Point-of-use fortification with micronutrient powders containing vitamins and minerals with or without iron.

PRIMARY AND SECONDARY OUTCOME MEASURES

Generalised linear geostatistical models with a Matern spatial correlation function were used to analyse four infection response variables, defined using different combinations of inflammation (C-reactive protein, CRP >5 mg/L) and malaria parasitaemia. Analyses were also stratified by treatment group to assess the independent effects of the iron intervention.

RESULTS

The by-group and combined-group analyses both showed that baseline infection status was the most consistent predictor of endline infection risk, particularly when infection was defined using parasitaemia. In the No-iron group, age above 24 months and weight-for-length z-score at baseline were associated with high CRP at endline. Higher asset score was associated with a 12% decreased odds of endline infection, defined as CRP >5 mg/L and/or parasitaemia (OR 0.88, 95% credible interval 0.78 to 0.98), regardless of group. Maps of the predicted risk and spatial random effects showed a defined low-risk area around the District centre, regardless of how infection was defined.

CONCLUSION

In a clinical trial setting of iron fortification, where all children receive treated bed nets and access to malaria treatment, there may be geographical variation in the risk of infection with distinct high-risk and low-risk areas, particularly around municipal centres.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov, NCT01001871.

Using low-cost drones to map malaria vector habitats

BACKGROUND

There is a growing awareness that if we are to achieve the ambitious goal of malaria elimination, we must compliment indoor-based vector control interventions (such as bednets and indoor spraying) with outdoor-based interventions such as larval source management (LSM). The effectiveness of LSM is limited by our capacity to identify and map mosquito aquatic habitats. This study provides a proof of concept for the use of a low-cost (< $1000) drone (DJI Phantom) for mapping water bodies in seven sites across Zanzibar including natural water bodies, irrigated and non-irrigated rice paddies, peri-urban and urban locations.

RESULTS

With flying times of less than 30 min for each site, high-resolution (7 cm) georeferenced images were successfully generated for each of the seven sites, covering areas up to 30 ha. Water bodies were readily identifiable in the imagery, as well as ancillary information for planning LSM activities (access routes to water bodies by road and foot) and public health management (e.g. identification of drinking water sources, mapping individual households and the nature of their construction).

CONCLUSION

The drone-based surveys carried out in this study provide a low-cost and flexible solution to mapping water bodies for operational dissemination of LSM initiatives in mosquito vector-borne disease elimination campaigns. Generated orthomosaics can also be used to provide vital information for other public health planning activities.

Location, location, location: environmental factors better predict malaria-positive individuals during reactive case detection than index case demographics in Southern Province, Zambia

Background

Decreasing malaria transmission leads to increasing heterogeneity with increased risk in both hot spots (locations) and hot pops (certain demographics). In Southern Province, Zambia, reactive case detection has formed a part of malaria surveillance and elimination efforts since 2011. Various factors may be associated with finding malaria infections during case investigations, including the demographics of the incident case and environmental characteristics of the location of the incident case.

Methods

Community health worker registries were used to determine what factors were associated with finding a malaria infection during reactive case detection.

Results

Location was a more powerful predictor of finding malaria infections during case investigations than the demographics of the incident case. After accounting for environmental characteristics, no demographics around the incident case were associated with finding malaria infections during case investigations. Various time-invariant measures of the environment, such as median enhanced vegetation index, the topographic position index, the convergence index, and the topographical wetness index, were all associated as expected with increased probability of finding a malaria infection during case investigations.

Conclusions

These results suggest that targeting the locations highly at risk of malaria transmission is of importance in elimination settings.

An integrated risk and vulnerability assessment framework for climate change and malaria transmission in East Africa

BACKGROUND

Malaria is one of the key research concerns in climate change-health relationships. Numerous risk assessments and modelling studies provide evidence that the transmission range of malaria will expand with rising temperatures, adversely impacting on vulnerable communities in the East African highlands. While there exist multiple lines of evidence for the influence of climate change on malaria transmission, there is insufficient understanding of the complex and interdependent factors that determine the risk and vulnerability of human populations at the community level. Moreover, existing studies have had limited focus on the nature of the impacts on vulnerable communities or how well they are prepared to cope. In order to address these gaps, a systems approach was used to present an integrated risk and vulnerability assessment framework for studies of community level risk and vulnerability to malaria due to climate change.

RESULTS

Drawing upon published literature on existing frameworks, a systems approach was applied to characterize the factors influencing the interactions between climate change and malaria transmission. This involved structural analysis to determine influential, relay, dependent and autonomous variables in order to construct a detailed causal loop conceptual model that illustrates the relationships among key variables. An integrated assessment framework that considers indicators of both biophysical and social vulnerability was proposed based on the conceptual model.

CONCLUSIONS

A major conclusion was that this integrated assessment framework can be implemented using Bayesian Belief Networks, and applied at a community level using both quantitative and qualitative methods with stakeholder engagement. The approach enables a robust assessment of community level risk and vulnerability to malaria, along with contextually relevant and targeted adaptation strategies for dealing with malaria transmission that incorporate both scientific and community perspectives.

Mapping intra-urban transmission risk of dengue fever with big hourly cellphone data

Cellphone tracking has been recently integrated into risk assessment of disease transmission, because travel behavior of disease carriers can be depicted in unprecedented details. Still in its infancy, such an integration has been limited to: 1) risk assessment only at national and provincial scales, where intra-urban human movements are neglected, and 2) using irregularly logged cellphone data that miss numerous user movements. Furthermore, few risk assessments have considered positional uncertainty of cellphone data. This study proposed a new framework for mapping intra-urban disease risk with regularly logged cellphone tracking data, taking the dengue fever in Shenzhen city as an example. Hourly tracking records of 5.85 million cellphone users, combined with the random forest classification and mosquito activities, were utilized to estimate the local transmission risk of dengue fever and the importation risk through travels. Stochastic simulations were further employed to quantify the uncertainty of risk. The resultant maps suggest targeted interventions to maximally reduce dengue cases exported to other places, as well as appropriate interventions to contain risk in places that import them. Given the popularity of cellphone use in urbanized areas, this framework can be adopted by other cities to design spatio-temporally resolved programs for disease control.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R² = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity.

Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis

Background

Determining the spatial patterns of infection among young children living in a malaria-endemic area may provide a means of locating high-risk populations who could benefit from additional resources for treatment and improved access to healthcare. The objective of this secondary analysis of baseline data from a cluster-randomized trial among 1943 young Ghanaian children (6–35 months of age) was to determine the geo-spatial factors associated with malaria and non-malaria infection status.

Methods

Spatial analyses were conducted using a generalized linear geostatistical model with a Matern spatial correlation function and four definitions of infection status using different combinations of inflammation (C-reactive protein, CRP > 5 mg/L) and malaria parasitaemia (with or without fever). Potentially informative variables were included in a final model through a series of modelling steps, including: individual-level variables (Model 1); household-level variables (Model 2); and, satellite-derived spatial variables (Model 3). A final (Model 4) and maximal model (Model 5) included a set of selected covariates from Models 1 to 3.

Results

The final models indicated that children with inflammation (CRP > 5 mg/L) and/or any evidence of malaria parasitaemia at baseline were more likely to be under 2 years of age, stunted, wasted, live further from a health facility, live at a lower elevation, have less educated mothers, and higher ferritin concentrations (corrected for inflammation) compared to children without inflammation or parasitaemia. Similar results were found when infection was defined as clinical malaria or parasitaemia with/without fever (definitions 3 and 4). Conversely, when infection was defined using CRP only, all covariates were non-significant with the exception of baseline ferritin concentration. In Model 5, all infection definitions that included parasitaemia demonstrated a significant interaction between normalized difference vegetation index and land cover type. Maps of the predicted infection probabilities and spatial random effect showed defined high- and low-risk areas that tended to coincide with elevation and cluster around villages.

Conclusions

The risk of infection among young children in a malaria-endemic area may have a predictable spatial pattern which is associated with geographical characteristics, such as elevation and distance to a health facility.

Original trial registration clinicaltrials.gov (NCT01001871)

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1388-1) contains supplementary material, which is available to authorized users.

Fine-scale distribution modeling of avian malaria vectors in north-central Kansas

Infectious diseases increasingly play a role in the decline of wildlife populations. Vector-borne diseases, in particular, have been implicated in mass mortality events and localized population declines are threatening some species with extinction. Transmission patterns for vector-borne diseases are influenced by the spatial distribution of vectors and are therefore not uniform across the landscape. Avian malaria is a globally distributed vector-borne disease that has been shown to affect endemic bird populations of North America. We evaluated shared habitat use between avian malaria vectors, mosquitoes in the genus Culex and a native grassland bird, the Greater Prairie-Chicken (Tympanuchus cupido), by (1) modeling the distribution of Culex spp. occurrence across the Smoky Hills of north-central Kansas using detection data and habitat variables, (2) assessing the occurrence of these vectors at nests of female Greater Prairie-Chickens, and (3) evaluating if shared habitat use between vectors and hosts is correlated with malarial infection status of the Greater Prairie-Chicken. Our results indicate that Culex occurrence increased at nest locations compared to other available but unoccupied grassland habitats; however the shared habitat use between vectors and hosts did not result in an increased prevalence of malarial parasites in Greater Prairie-Chickens that occupied habitats with high vector occurrence. We developed a predictive map to illustrate the associations between Culex occurrence and infection status with malarial parasites in an obligate grassland bird that may be used to guide management decisions to limit the spread of vector-borne diseases.

Comparing ownership and use of bed nets at two sites with differential malaria transmission in western Kenya

Background

Challenges persist in ensuring access to and optimal use of long-lasting, insecticidal bed nets (LLINs). Factors associated with ownership and use may differ depending on the history of malaria and prevention control efforts in a specific region. Understanding how the cultural and social-environmental context of bed net use may differ between high- and low-risk regions is important when identifying solutions to improve uptake and appropriate use.

Methods

Community forums and a household, cross-sectional survey were used to collect information on factors related to bed net ownership and use in western Kenya. Sites with disparate levels of transmission were selected, including an endemic lowland area, Miwani, and a highland epidemic-prone area, Kapkangani. Analysis of ownership was stratified by site. A combined site analysis was conducted to examine factors associated with use of all available bed nets. Logistic regression modelling was used to determine factors associated with ownership and use of owned bed nets.

Results

Access to bed nets as the leading barrier to their use was identified in community forums and cross-sectional surveys. While disuse of available bed nets was discussed in the forums, it was a relatively rare occurrence in both sites. Factors associated with ownership varied by site. Education, perceived risk of malaria and knowledge of individuals who had died of malaria were associated with higher bed net ownership in the highlands, while in the lowlands individuals reporting it was easy to get a bed net were more likely to own one. A combined site analysis indicated that not using an available bed net was associated with the attitudes that taking malaria drugs is easier than using a bed net and that use of a bed net will not prevent malaria. In addition, individuals with an unused bed net in the household were more likely to indicate that bed nets are difficult to use, that purchased bed nets are better than freely distributed ones, and that bed nets should only be used during the rainy season.

Conclusion

Variations in factors associated with ownership should be acknowledged when constructing messaging and distribution campaigns. Despite reports of bed nets being used for other purposes, those in the home were rarely unused in these communities. Disuse seemed to be related to beliefs that can be addressed through education programmes. As mass distributions continue to take place, additional research is needed to determine if factors associated with LLIN ownership and use change with increasing availability of LLIN.

Mapping residual transmission for malaria elimination

Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections. In the last phases of malaria elimination, as universal interventions reap diminishing marginal returns, national resources must become increasingly devoted to identifying where residual transmission is occurring. The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk. Using routine national surveillance data from Swaziland (a sub-Saharan country on the verge of elimination), we estimated individual reproductive numbers. Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show ‘malariogenic potential’, a first for malaria elimination. As countries approach elimination, these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination.

DOI:http://dx.doi.org/10.7554/eLife.09520.001

Swaziland has set a national goal of eliminating malaria transmission in the very short term, which would make it the first country in sub-Saharan Africa to do so. More than half of the cases of malaria that are observed in Swaziland are caused by infections picked up by travelers while they were in other countries where the disease is much more prevalent. The other cases – people who became infected in Swaziland – are the cases that the government of Swaziland is trying to prevent.

If Swaziland is going to eliminate malaria, it will need to identify any places where the malaria parasites are still spreading throughout the population so it can target those communities with effective prevention measures. It will also need to manage the risk that infections imported from abroad may re-start transmission in places where it has been stopped.

To work out how likely it is that a malaria infection will be transmitted by mosquitoes in a particular place, researchers can look at past malaria data and calculate how many new infections are caused by each case. Reiner et al. have now produced a computer model that estimates how this number varies across Swaziland, highlighting places where the government is going to need to focus efforts to eliminate malaria. The model shows that in some rural areas near Mozambique, each individual infected with malaria is causing more than one other person to become infected. This confirms that the disease has not yet been eliminated from these areas. However, in other regions of the country, malaria rarely spreads between individuals.

The detailed regional information from the model may help public health authorities in Swaziland better target their anti-malaria resources. In large cities where most cases are imported, Reiner et al. suggest focusing resources on providing preventive treatment to travelers who plan on visiting places where malaria is spreading. However, in rural areas where malaria continues to spread, preventively treating the whole population or providing them with tools to protect them from mosquitoes might be more appropriate. Similar considerations of regional differences in the spread of malaria could also help other countries to more effectively combat the disease.

DOI:http://dx.doi.org/10.7554/eLife.09520.002

Decline in childhood iron deficiency after interruption of malaria transmission in highland Kenya

BACKGROUND

Achieving optimal iron status in children in malaria-endemic areas may increase the risk of malaria. Malaria itself may contribute to iron deficiency, but the impact of an interruption in malaria transmission on the prevalence of iron deficiency is unknown.

OBJECTIVES

We aimed to determine whether 1) iron status improved in children living in 2 Kenyan villages with a documented cessation in malaria transmission and 2) changes in iron status correlated with changes in hemoglobin.

DESIGN

We measured iron [hemoglobin, ferritin, soluble transferrin receptor (sTfR)] and inflammatory [C-reactive protein (CRP)] markers in paired plasma samples from 190 children aged 4-59 mo at the beginning (May 2007) and end (July 2008) of a documented 12-mo period of interruption in malaria transmission in 2 highland areas in Kenya with unstable malaria transmission and ongoing malaria surveillance.

RESULTS

Between May 2007 and July 2008, mean (±SD) hemoglobin increased from 10.8 ± 1.6 to 11.6 ± 1.6 g/dL. Median (25th, 75th percentile) ferritin increased from 17.0 (9.7, 25.6) to 22.6 (13.4, 34.7) μg/L (P < 0.001), whereas median sTfR decreased from 32.4 (26.3, 43.2) to 27.7 (22.1, 36.0) nmol/L (P < 0.001). Median CRP was low (<1 mg/L in both years) and did not change significantly. Iron deficiency prevalence (ferritin <12 μg/L, or <30 μg/L if CRP ≥10 mg/L) decreased from 35.9% (95% CI: 28.9%, 43.0%) to 24.9% (18.5%, 31.2%) (P = 0.005). The prevalence of iron deficiency with anemia (hemoglobin <11.0 g/dL) declined from 27.2% (20.7%, 33.8%) to 12.2% (7.4%, 17.1%) (P < 0.001). Improvement in iron status correlated with an increase in hemoglobin and was greater than explained by physiologic changes expected with age.

CONCLUSIONS

In this area of unstable malaria transmission, the prevalence of iron deficiency in children decreased significantly after the interruption of malaria transmission and was correlated with an increase in hemoglobin. These findings suggest that malaria elimination strategies themselves may be an effective way to address iron deficiency in malaria-endemic areas.

Patterns of spatio-temporal distribution, abundance, and diversity in a mosquito community from the eastern Smoky Hills of Kansas

Nearly 30% of emerging infectious disease events are caused by vector-borne pathogens with wildlife origins. Their transmission involves a complex interplay among pathogens, arthropod vectors, the environment and host species, and they pose a risk for public health, livestock and wildlife species. Examining habitat associations of vector species known to transmit infectious diseases, and quantifying spatio-temporal dynamics of mosquito vector communities is one aspect of the holistic One Health approach that is necessary to develop effective control measures. A survey was conducted from May to August, 2010 of the abundance and diversity of mosquito species occurring in the mixed-grass prairie habitat of the Smoky Hills of Kansas. This region is an important breeding ground for North America's grassland nesting birds and, as such, it could represent an important habitat for the enzootic amplification cycle of avian malaria and infectious encephalitides, as well as spill-over events to humans and livestock. A total of 11 species, belonging to the three genera Aedes, Anopheles, and Culex, was collected during this study. Aedes nigromaculis, Ae. sollicitans, Ae. taeniorhynchus, Culex salinarius, and Cx. tarsalis accounted for 98% of the collected species. Multiple linear regression models suggested that mosquito abundances in the grasslands of the central Great Plains were explained by meteorological and environmental variables. Temporal dynamics in mosquito abundances were well supported by models that included maximum and minimum temperature indices (adjusted R(2) = 0.73). Spatial dynamics of mosquito abundances were best explained by a model containing the following environmental variables (adjusted R(2) =0.37): ground curvature, topographic wetness index, distance to woodland, and distance to road. The mosquito species we detected are known vectors for infectious encephalitides, including West Nile virus. Understanding the microhabitat characteristics of these mosquito species in a grassland ecosystem will aid in the control and management of these disease vectors.

Risk factors associated with clinical malaria episodes in Bangladesh: a longitudinal study

Malaria is endemic to Bangladesh. In this longitudinal study, we used hydrologic, topographic, and socioeconomic risk factors to explain single and multiple malaria infections at individual and household levels. Malaria incidence was determined for 1,634 households in 54 villages in 2009 and 2010. During the entire study period 21.8% of households accounted for all (n = 497) malaria cases detected; 15.4% of households had 1 case and 6.4% had ≥ 2 cases. The greatest risk factors for malaria infection were low bed net ratio per household, house construction materials (wall), and high density of houses. Hydrologic and topographic factors were not significantly associated with malaria risk. This study identifies stable malaria hotspots and risk factors that should be considered for cost-effective targeting of malaria interventions that may contribute to potential elimination of malaria in Bangladesh.

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland

Background

As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.

Methods

Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season.

Results

Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012.

Conclusions

The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers

BACKGROUND

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae.

METHODS

Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC).

RESULTS

All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable.

CONCLUSIONS

Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Development of clinical immunity to malaria in highland areas of low and unstable transmission

In highland areas of unstable, low malaria transmission, the extent to which immunity to uncomplicated malaria develops with age and intermittent parasite exposure has not been well characterized. We conducted active surveillance for clinical malaria during April 2003-March 2005 in two highland areas of western Kenya (Kapsisiywa and Kipsamoite). In both sites, annual malaria incidence was significantly lower in persons ≥ 15 years of age than in persons < 5 years of age (Kapsisiywa: incidence = 382.9 cases/1,000 persons among persons < 1-4 years of age versus 135.1 cases/1,000 persons among persons ≥ 15 years of age; Kipsamoite: incidence = 233.0 cases/1,000 persons in persons < 1-4 years of age versus 43.3 cases/1,000 persons in persons ≥ 15 years of age). In Kapsisiywa, among persons with malaria, parasite density and axillary body temperature were also significantly lower in persons ≥ 15 years of age than in persons < 5 years of age. Even in highland areas of unstable and low malaria transmission, age is associated with development of clinical immunity to malaria.

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia

To understand the drivers and consequences of malaria in epidemic-prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad-scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999-2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.

Mapping malaria risk among children in Côte d'Ivoire using Bayesian geo-statistical models

BACKGROUND

In Côte d'Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d'Ivoire at high spatial resolution.

METHODS

Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d'Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d'Ivoire, including uncertainty.

RESULTS

Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas.

CONCLUSION

The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d'Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.

Spatial analysis and mapping of malaria risk in an endemic area, south of Iran: a GIS based decision making for planning of control

Bashagard district is one of the important malaria endemic areas in southern Iran. From this region a total of 16,199 indigenous cases have been reported in recent years. The aim of this study was to determine the situation of the disease and provide the risk map for the area. ArcGIS9.2 was used for mapping spatial distribution of malaria incidence. Hot spots were obtained using evidence-based weighting method for transmission risk. Environmental factors including temperature, relative humidity, altitude, slope and distance to rivers were combined by weighted multi criteria evaluation for mapping malaria hazard area at the district level. Similarly, risk map was developed by overlaying weighted hazard, land use/land cover, population density, malaria incidence, development factors and intervention methods. Our results reveal that the disease mainly occurs in north and east of the study area. Consequently the district is divided into three strata. Appropriate interventions are recommended for each stratum based on national malaria policy. Malaria hazard and risk map, stratification based on relevant information and data analyzing provide a useful method preparedness and early warning system for malaria control, although regular updating is required timely.

Geographical mapping and Bayesian spatial modeling of malaria incidence in Sistan and Baluchistan province, Iran

OBJECTIVE

To present the geographical map of malaria and identify some of the important environmental factors of this disease in Sistan and Baluchistan province, Iran.

METHODS

We used the registered malaria data to compute the standard incidence rates (SIRs) of malaria in different areas of Sistan and Baluchistan province for a nine-year period (from 2001 to 2009). Statistical analyses consisted of two different parts: geographical mapping of malaria incidence rates, and modeling the environmental factors. The empirical Bayesian estimates of malaria SIRs were utilized for geographical mapping of malaria and a Poisson random effects model was used for assessing the effect of environmental factors on malaria SIRs.

RESULTS

In general, 64,926 new cases of malaria were registered in Sistan and Baluchistan Province from 2001 to 2009. Among them, 42,695 patients (65.8%) were male and 22,231 patients (34.2%) were female. Modeling the environmental factors showed that malaria incidence rates had positive relationship with humidity, elevation, average minimum temperature and average maximum temperature, while rainfall had negative effect on malaria SIRs in this province.

CONCLUSIONS

The results of the present study reveals that malaria is still a serious health problem in Sistan and Baluchistan province, Iran. Geographical map and related environmental factors of malaria can help the health policy makers to intervene in high risk areas more efficiently and allocate the resources in a proper manner.

Topography as a modifier of breeding habitats and concurrent vulnerability to malaria risk in the western Kenya highlands

BACKGROUND

Topographic parameters such as elevation, slope, aspect, and ruggedness play an important role in malaria transmission in the highland areas. They affect biological systems, such as larval habitats presence and productivity for malaria mosquitoes. This study investigated whether the distribution of local spatial malaria vectors and risk of infection with malaria parasites in the highlands is related to topography.

METHODS

Four villages each measuring 9 Km2 lying between 1400-1700 m above sea level in the western Kenya highlands were categorized into a pair of broad and narrow valley shaped terrain sites. Larval, indoor resting adult malaria vectors and infection surveys were collected originating from the valley bottom and ending at the hilltop on both sides of the valley during the rainy and dry seasons. Data collected at a distance of ≤ 500 m from the main river/stream were categorized as valley bottom and those above as uphill. Larval surveys were categorized by habitat location while vectors and infections by house location.

RESULTS

Overall, broad flat bottomed valleys had a significantly higher number of anopheles larvae/dip in their habitats than in narrow valleys during both the dry (1.89 versus 0.89 larvae/dip) and the rainy season (1.66 versus 0.89 larvae/dip). Similarly, vector adult densities/house in broad valley villages were higher than those within narrow valley houses during both the dry (0.64 versus 0.40) and the rainy season (0.96 versus 0.09). Asymptomatic malaria prevalence was significantly higher in participants residing within broad than those in narrow valley villages during the dry (14.55% vs. 7.48%) and rainy (17.15% vs. 1.20%) season. Malaria infections were wide spread in broad valley villages during both the dry and rainy season, whereas over 65% of infections were clustered at the valley bottom in narrow valley villages during both seasons.

CONCLUSION

Despite being in the highlands, local areas within low gradient topography characterized by broad valley bottoms have stable and significantly high malaria risk unlike those with steep gradient topography, which exhibit seasonal variations. Topographic parameters could therefore be considered in identification of high-risk malaria foci to help enhance surveillance or targeted control activities in regions where they are most needed.

Anopheles atroparvus density modeling using MODIS NDVI in a former malarious area in Portugal

Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the model's ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.

Decreased prevalence of anemia in highland areas of low malaria transmission after a 1-year interruption of transmission

BACKGROUND

Malaria control campaigns have reduced malaria transmission to very low levels in many areas of Africa. Yet the extent to which malaria interruption or elimination might decrease the prevalence of anemia in areas of low malaria transmission is unknown.

METHODS

Kapsisiywa and Kipsamoite, highland areas of Kenya with low, unstable malaria transmission, experienced a 12-month interruption in malaria transmission from April 2007 to May 2008, following high-level coverage (>70% of households) with indoor residual insecticide spraying in 2007. Hemoglobin levels were tested in 1697 randomly selected asymptomatic residents of Kapsisiywa (n = 910) and Kipsamoite (n = 787) at the beginning of a 12-month period of interrupted transmission (in May 2007) and 14 months later (in July 2008).

RESULTS

From May 2007 to July 2008, only 1 of 1697 study cohort members developed clinical malaria. In this period, the prevalence of anemia decreased in Kapsisiywa in all age groups (from 57.5% to 37.9% in children aged <5 years [P < .001], from 21.7% to 10.5% in children aged 5-14 years [P < .001], and from 22.7% to 16.6% in individuals aged ≥ 15 years [P = .004]). The prevalence of anemia in Kipsamoite also decreased in children aged <5 years (from 47.2% to 31.3%; P = .001) but was unchanged in children aged 5-14 years and in individuals aged ≥15 years. Among children <5 years, anemia prevalence was reduced by 34% in both Kapsisiywa (95% confidence interval [CI], 21%-45%) and Kipsamoite (95% CI, 16%-48%).

CONCLUSIONS

Successful malaria elimination or interruption may lead to substantial reductions in anemia prevalence even in areas of very low transmission.

Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey

Background

The burden of malaria has decreased dramatically within the past several years in parts of sub-Saharan Africa. Further malaria control will require targeted control strategies based on evidence of risk. The objective of this study was to identify environmental risk factors for malaria transmission using remote sensing technologies to guide malaria control interventions in a region of declining burden of malaria.

Methods

Satellite images were used to construct a sampling frame for the random selection of households enrolled in prospective longitudinal and cross-sectional surveys of malaria parasitaemia in Southern Province, Zambia. A digital elevation model (DEM) was derived from the Shuttle Radar Topography Mission version 3 DEM and used for landscape characterization, including landforms, elevation, aspect, slope, topographic wetness, topographic position index and hydrological models of stream networks.

Results

A total of 768 individuals from 128 randomly selected households were enrolled over 21 months, from the end of the rainy season in April 2007 through December 2008. Of the 768 individuals tested, 117 (15.2%) were positive by malaria rapid diagnostic test (RDT). Individuals residing within 3.75 km of a third order stream were at increased risk of malaria. Households at elevations above the baseline elevation for the region were at decreasing risk of having RDT-positive residents. Households where new infections occurred were overlaid on a risk map of RDT positive households and incident infections were more likely to be located in high-risk areas derived from prevalence data. Based on the spatial risk map, targeting households in the top 80^th percentile of malaria risk would require malaria control interventions directed to only 24% of the households.

Conclusions

Remote sensing technologies can be used to target malaria control interventions in a region of declining malaria transmission in southern Zambia, enabling a more efficient use of resources for malaria elimination.

Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands

BACKGROUND

Identification of high-risk malaria foci can help enhance surveillance or control activities in regions where they are most needed. Associations between malaria risk and land-use/land-cover are well-recognized, but these environmental characteristics are closely interrelated with the land's topography (e.g., hills, valleys, elevation), which also influences malaria risk strongly. Parsing the individual contributions of land-cover/land-use variables to malaria risk requires examining these associations in the context of their topographic landscape. This study examined whether environmental factors like land-cover, land-use, and urban density improved malaria risk prediction based solely on the topographically-determined context, as measured by the topographic wetness index.

METHODS

The topographic wetness index, an estimate of predicted water accumulation in a defined area, was generated from a digital terrain model of the landscape surrounding households in two neighbouring western Kenyan highland communities. Variables determined to best encompass the variance in this topographic wetness surface were calculated at a household level. Land-cover/land-use information was extracted from a high-resolution satellite image using an object-based classification method. Topographic and land-cover variables were used individually and in combination to predict household-level malaria in the communities through an iterative split-sample model fitting and testing procedure. Models with only topographic variables were compared to those with additional predictive factors related to land-cover/land-use to investigate whether these environmental factors improved prediction of malaria based on the shape of the land alone.

RESULTS

Variables related to topographic wetness proved most useful in predicting the households of individuals contracting malaria in this region of rugged terrain. Other variables related to human modification of the environment also demonstrated clear associations with household malaria. However, these land-cover/land-use variables failed to produce unambiguous improvements in statistical predictive models controlling for important topographic factors, with none improving prediction of household-level malaria more than 75% of the time.

CONCLUSIONS

Topographic wetness values in this region of highly varied terrain more accurately predicted houses at greater risk of malaria than did consideration of land-cover/land-use characteristics. As such, those planning control or local elimination strategies in similar highland regions may use topographic and geographic characteristics to effectively identify high-receptivity regions that may require enhanced vigilance.

Mapping malaria risk in Bangladesh using Bayesian geostatistical models

Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR(2-10)) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR(2-10) was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.

Geographical and environmental approaches to urban malaria in Antananarivo (Madagascar)

Background

Previous studies, conducted in the urban of Antananarivo, showed low rate of confirmed malaria cases. We used a geographical and environmental approach to investigate the contribution of environmental factors to urban malaria in Antananarivo.

Methods

Remote sensing data were used to locate rice fields, which were considered to be the principal mosquito breeding sites. We carried out supervised classification by the maximum likelihood method. Entomological study allowed vector species determination from collected larval and adult mosquitoes. Mosquito infectivity was studied, to assess the risk of transmission, and the type of mosquito breeding site was determined. Epidemiological data were collected from November 2006 to December 2007, from public health centres, to determine malaria incidence. Polymerase chain reaction was carried out on dried blood spots from patients, to detect cases of malaria. Rapid diagnostic tests were used to confirm malaria cases among febrile school children in a school survey.

A geographical information system was constructed for data integration. Altitude, temperature, rainfall, population density and rice field surface area were analysed and the effects of these factors on the occurrence of confirmed malaria cases were studied.

Results

Polymerase chain reaction confirmed malaria in 5.1% of the presumed cases. Entomological studies showed An. arabiensis as potential vector. Rice fields remained to be the principal breeding sites. Travel report was considered as related to the occurrence of P. falciparum malaria cases.

Conclusion

Geographical and environmental factors did not show direct relationship with malaria incidence but they seem ensuring suitability of vector development. Absence of relationship may be due to a lack of statistical power. Despite the presence of An. arabiensis, scarce parasitic reservoir and rapid access to health care do not constitute optimal conditions to a threatening malaria transmission. However, imported malaria case is suggestive to sustain the pocket transmission in Antananarivo.

Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu

Background

The Ministry of Health in the Republic of Vanuatu has implemented a malaria elimination programme in Tafea Province, the most southern and eastern limit of malaria transmission in the South West Pacific. Tafea Province is comprised of five islands with malaria elimination achieved on one of these islands (Aneityum) in 1998. The current study aimed to establish the baseline distribution of malaria on the most malarious of the province's islands, Tanna Island, to guide the implementation of elimination activities.

Methods

A parasitological survey was conducted in Tafea Province in 2008. On Tanna Island there were 4,716 participants from 220 villages, geo-referenced using a global position system. Spatial autocorrelation in observed prevalence values was assessed using a semivariogram. Backwards step-wise regression analysis was conducted to determine the inclusion of environmental and climatic variables into a prediction model. The Bayesian geostatistical logistic regression model was used to predict malaria risk, and associated uncertainty across the island.

Results

Overall, prevalence on Tanna was 1.0% for Plasmodium falciparum (accounting for 32% of infections) and 2.2% for Plasmodium vivax (accounting for 68% of infections). Regression analysis showed significant association with elevation and distance to coastline for P. vivax and P. falciparum, but no significant association with NDVI or TIR. Colinearity was observed between elevation and distance to coastline with the later variable included in the final Bayesian geostatistical model for P. vivax and the former included in the final model for P. falciparum. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability.

Conclusion

Malaria in Tanna Island, Vanuatu, has a focal and predominantly coastal distribution. As Vanuatu refines its elimination strategy, malaria risk maps represent an invaluable resource in the strategic planning of all levels of malaria interventions for the island.

Climate change and highland malaria: fresh air for a hot debate

In recent decades, malaria has become established in zones at the margin of its previous distribution, especially in the highlands of East Africa. Studies in this region have sparked a heated debate over the importance of climate change in the territorial expansion of malaria, where positions range from its neglect to the reification of correlations as causes. Here, we review studies supporting and rebutting the role of climatic change as a driving force for highland invasion by malaria. We assessed the conclusions from both sides of the argument and found that evidence for the role of climate in these dynamics is robust. However, we also argue that over-emphasizing the importance of climate is misleading for setting a research agenda, even one which attempts to understand climate change impacts on emerging malaria patterns. We review alternative drivers for the emergence of this disease and highlight the problems still calling for research if the multidimensional nature of malaria is to be adequately tackled. We also contextualize highland malaria as an ongoing evolutionary process. Finally, we present Schmalhausen's law, which explains the lack of resilience in stressed systems, as a biological principle that unifies the importance of climatic and other environmental factors in driving malaria patterns across different spatio-temporal scales.

High resolution niche models of malaria vectors in northern Tanzania: a new capacity to predict malaria risk?

BACKGROUND

Malaria transmission rates in Africa can vary dramatically over the space of a few kilometres. This spatial heterogeneity reflects variation in vector mosquito habitat and presents an important obstacle to the efficient allocation of malaria control resources. Malaria control is further complicated by combinations of vector species that respond differently to control interventions. Recent modelling innovations make it possible to predict vector distributions and extrapolate malaria risk continentally, but these risk mapping efforts have not yet bridged the spatial gap to guide on-the-ground control efforts.

METHODOLOGY/PRINCIPAL FINDINGS

We used Maximum Entropy with purpose-built, high resolution land cover data and other environmental factors to model the spatial distributions of the three dominant malaria vector species in a 94,000 km(2) region of east Africa. Remotely sensed land cover was necessary in each vector's niche model. Seasonality of precipitation and maximum annual temperature also contributed to niche models for Anopheles arabiensis and An. funestus s.l. (AUC 0.989 and 0.991, respectively), but cold season precipitation and elevation were important for An. gambiae s.s. (AUC 0.997). Although these niche models appear highly accurate, the critical test is whether they improve predictions of malaria prevalence in human populations. Vector habitat within 1.5 km of community-based malaria prevalence measurements interacts with elevation to substantially improve predictions of Plasmodium falciparum prevalence in children. The inclusion of the mechanistic link between malaria prevalence and vector habitat greatly improves the precision and accuracy of prevalence predictions (r(2) = 0.83 including vector habitat, or r(2) = 0.50 without vector habitat). Predictions including vector habitat are unbiased (observations vs. model predictions of prevalence: slope = 1.02). Using this model, we generate a high resolution map of predicted malaria prevalence throughout the study region.

CONCLUSIONS/SIGNIFICANCE

The interaction between mosquito niche space and microclimate along elevational gradients indicates worrisome potential for climate and land use changes to exacerbate malaria resurgence in the east African highlands. Nevertheless, it is possible to direct interventions precisely to ameliorate potential impacts.

Possible interruption of malaria transmission, highland Kenya, 2007-2008

Annual insecticide spraying and artemisinin combination therapy may stop transmission.

Highland areas where malaria transmission is unstable are targets for malaria elimination because transmission decreases to low levels during the dry season. In highland areas of Kipsamoite and Kapsisiywa, Kenya (population ≈7,400 persons), annual household indoor residual spraying with a synthetic pyrethroid was performed starting in 2005, and artemether/lumefantrine was implemented as first-line malaria treatment in October 2006. During April 2007–March 2008, no microscopy-confirmed cases of malaria occurred at the sites. In 4 assessments of asymptomatic persons during May 2007–April 2008, a total of <0.3% of persons were positive for asexual Plasmodium falciparum by microscopy or PCR at any time, and none were positive by PCR at the last 2 sample collections. Our findings show that in such areas, interruption and eventual elimination of malaria transmission may be achievable with widespread annual indoor residual spraying of households and artemisinin combination therapy.

Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case-control study

OBJECTIVE

To identify risk factors for uncomplicated malaria in highland areas of East Africa at higher risk of malaria epidemics, in order to design appropriate interventions.

METHODS

Prospective, population-based, case-control study in the Nandi Hills, a highland area of western Kenya, to identify environmental, sociodemographic and behavioural factors associated with clinical malaria. Data were collected using field observation, a structured questionnaire, and a global positioning system device.

RESULTS

We interviewed 488 cases of slide-confirmed malaria and 980 age-matched controls. Multivariate analyses associated higher malaria risk with living <250 m of a forest [OR = 3.3 (95% CI 1.5, 7.1)], <250 m of a swamp [2.8 (1.3, 5.9)], <200 m of maize fields [2.0 (1.2, 3.4)], in the absence of trees <200 m [1.6 (1.2, 2.2)], on flat land [1.6 (1.2, 2.2)], in houses without ceilings [1.5 (1.1, 2.2)], in houses with a separate kitchen building [1.8 (1.4, 2.3)] and in households where the female household head had no education [1.9 (1.1, 3.1)]. Travelling out of the study site [2.2 (1.2, 4.1)] was also associated with increased risk. CONCLUSIONS; In this East African highland area, risk of developing uncomplicated malaria was multifactorial with a risk factor profile similar to that in endemic regions. Households within close proximity to forest and swamp borders are at higher risk of malaria and should be included in indoor residual spraying campaigns.

Spatial risk profiling of Plasmodium falciparum parasitaemia in a high endemicity area in Côte d'Ivoire

BACKGROUND

The objective of this study was to identify demographic, environmental and socioeconomic risk factors and spatial patterns of Plasmodium falciparum parasitaemia in a high endemicity area of Africa, and to specify how this information can facilitate improved malaria control at the district level.

METHODS

A questionnaire was administered to about 4,000 schoolchildren in 55 schools in western Côte d'Ivoire to determine children's socioeconomic status and their habit of sleeping under bed nets. Environmental data were obtained from satellite images, digitized ground maps and a second questionnaire addressed to school directors. Finger prick blood samples were collected and P. falciparum parasitaemia determined under a microscope using standardized, quality-controlled methods. Bayesian variogram models were utilized for spatial risk modelling and mapping of P. falciparum parasitaemia at non-sampled locations, assuming stationary and non-stationary underlying spatial dependence.

RESULTS

Two-thirds of the schoolchildren were infected with P. falciparum and the mean parasitaemia among infected children was 959 parasites/microl of blood. Age, socioeconomic status, not sleeping under a bed net, coverage rate with bed nets and environmental factors (e.g., normalized difference vegetation index, rainfall, land surface temperature and living in close proximity to standing water) were significantly associated with the risk of P. falciparum parasitaemia. After accounting for spatial correlation, age, bed net coverage, rainfall during the main malaria transmission season and distance to rivers remained significant covariates.

CONCLUSION

It is argued that a massive increase in bed net coverage, particularly in villages in close proximity to rivers, in concert with other control measures, is necessary to bring malaria endemicity down to intermediate or low levels.

What you see is not what you get: implications of the brevity of antibody responses to malaria antigens and transmission heterogeneity in longitudinal studies of malaria immunity

Background

A major handicap in developing a malaria vaccine is the difficulty in pinpointing the immune responses that protect against malaria. The protective efficacy of natural or vaccine-induced immune responses against malaria is normally assessed by relating the level of the responses in an individual at the beginning of a follow-up period and the individual's experience of malaria infection or disease during the follow-up. This approach has identified a number of important responses against malaria, but their protective efficacies vary considerably between studies.

Hypothesis

It is likely that apart from differences in study methodologies, differences in exposure among study subjects within each study and brevity of antibody responses to malaria antigen are important sources of the variation in protective efficacy of anti-malaria immune responses mentioned above. Since malaria immunity is not complete, anyone in an area of stable malaria transmission who does not become asymptomatically or symptomatically infected during follow-up subsequent to treatment is most likely unexposed rather than immune.

Testing the hypothesis

It is proposed that individuals involved in a longitudinal study of malaria immunity should be treated for malaria prior to the start of the study and only those who present with at least an asymptomatic infection during the follow-up should be included in the analysis. In addition, it is proposed that more closely repeated serological survey should be carried out during follow-up in order to get a better picture of an individual's serological status.

Implications of the hypothesis

Failure to distinguish between individuals who do not get a clinical episode during follow-up because they were unexposed and those who are genuinely immune undermines our ability to assign a protective role to immune responses against malaria. The brevity of antibodies responses makes it difficult to assign the true serological status of an individual at any given time, i.e. those positive at a survey may be negative by the time they encounter the next infection.

Habitat stability and occurrences of malaria vector larvae in western Kenya highlands

Background

Although the occurrence of malaria vector larvae in the valleys of western Kenya highlands is well documented, knowledge of larval habitats in the uphill sites is lacking. Given that most inhabitants of the highlands actually dwell in the uphill regions, it is important to develop understanding of mosquito breeding habitat stability in these sites in order to determine their potential for larval control.

Methods

A total of 128 potential larval habitats were identified in hilltops and along the seasonal streams in the Sigalagala area of Kakamega district, western Kenya. Water availability in the habitats was followed up daily from August 3, 2006 to February 23, 2007. A habitat is defined as stable when it remains aquatic continuously for at least 12 d. Mosquito larvae were observed weekly. Frequencies of aquatic, stable and larvae positive habitats were compared between the hilltop and seasonal stream area using χ²-test. Factors affecting the presence/absence of Anopheles gambiae larvae in the highlands were determined using multiple logistic regression analysis.

Results

Topography significantly affected habitat availability and stability. The occurrence of aquatic habitats in the hilltop was more sporadic than in the stream area. The percentage of habitat occurrences that were classified as stable during the rainy season is 48.76% and 80.79% respectively for the hilltop and stream area. Corresponding frequencies of larvae positive habitats were 0% in the hilltop and 5.91% in the stream area. After the rainy season, only 23.42% of habitat occurrences were stable and 0.01% larvae positive habitats were found in the hilltops, whereas 89.75% of occurrences remained stable in the stream area resulting in a frequency of 12.21% larvae positive habitats. The logistic regression analysis confirmed the association between habitat stability and larval occurrence and indicated that habitat surface area was negatively affecting the occurrence of An. gambiae larvae. While An. gambiae and An. funestus larvae occurred throughout the study period along the streams, a total of only 15 An. gambiae larvae were counted in the hilltops, and no An. funestus were found. Moreover, no larvae managed to develop into adults in the hilltops, and the density of adult An. gambiae was consistently low, averaging at 0.06 females per house per survey.

Conclusion

The occurrence of malaria vector larvae in the hilltop area was uncommon as a result of the low availability and high instability of habitats. To optimize the cost-effectiveness of malaria interventions in the western Kenya highlands, larval control should be focused primarily along the streams, as these are likely the only productive habitats at high altitude.