Further shrinking the malaria map: how can geospatial science help to achieve malaria elimination?

Malaria is one of the biggest contributors to deaths caused by infectious disease. More than 30 countries have planned or started programmes to target malaria elimination, often with explicit support from international donors. The spatial distribution of malaria, at all levels of endemicity, is heterogeneous. Moreover, populations living in low-endemic settings where elimination efforts might be targeted are often spatially heterogeneous. Geospatial methods, therefore, can help design, target, monitor, and assess malaria elimination programmes. Rapid advances in technology and analytical methods have allowed the spatial prediction of malaria risk and the development of spatial decision support systems, which can enhance elimination programmes by enabling accurate and timely resource allocation. However, no framework exists for assessment of geospatial instruments. Research is needed to identify measurable indicators of elimination progress and to quantify the effect of geospatial methods in achievement of elimination outcomes.

Characterizing the spatial and temporal variation of malaria incidence in Bangladesh, 2007.

BACKGROUND: Malaria remains a significant health problem in Bangladesh affecting 13 of 64 districts. The risk of malaria is variable across the endemic areas and throughout the year. A better understanding of the spatial and temporal patterns in malaria risk and the determinants driving the variation are crucial for the appropriate targeting of interventions under the National Malaria Control and Prevention Programme. METHODS: Numbers of Plasmodium falciparum and Plasmodium vivax malaria cases reported by month in 2007, across the 70 endemic thanas (sub-districts) in Bangladesh, were assembled from health centre surveillance reports. Bayesian Poisson regression models of incidence were constructed, with fixed effects for monthly rainfall, maximum temperature and elevation, and random effects for thanas, with a conditional autoregressive prior spatial structure. RESULTS: The annual incidence of reported cases was 34.0 and 9.6 cases/10,000 population for P. falciparum and P. vivax respectively and the population of the 70 malaria-endemic thanas was approximately 13.5 million in 2007. Incidence of reported cases for both types of malaria was highest in the mountainous south-east of the country (the Chittagong Hill Tracts). Models revealed statistically significant positive associations between the incidence of reported P. vivax and P. falciparum cases and rainfall and maximum temperature. CONCLUSIONS: The risk of P. falciparum and P. vivax was spatially variable across the endemic thanas of Bangladesh and also highly seasonal, suggesting that interventions should be targeted and timed according to the risk profile of the endemic areas. Rainfall, temperature and elevation are major factors driving the spatiotemporal patterns of malaria in Bangladesh.

Integrated rapid mapping of neglected tropical diseases in three States of South Sudan: survey findings and treatment needs.

BACKGROUND: Integrated rapid mapping to target interventions for schistosomiasis, soil-transmitted helminthiasis (STH) and lymphatic filariasis (LF) is ongoing in South Sudan. From May to September 2010, three states--Unity, Eastern Equatoria and Central Equatoria--were surveyed with the aim of identifying which administrative areas are eligible for mass drug administration (MDA) of preventive chemotherapy (PCT). METHODS AND PRINCIPAL FINDINGS: Payams (third administrative tier) were surveyed for Schistosoma mansoni, S. haematobium and STH infections while counties (second administrative tier) were surveyed for LF. Overall, 12,742 children from 193 sites were tested for schistosome and STH infection and, at a subset of 50 sites, 3,980 adults were tested for LF. Either S. mansoni or S. haematobium or both species were endemic throughout Unity State and occurred in foci in Central and Eastern Equatoria. STH infection was endemic throughout Central Equatoria and the western counties of Eastern Equatoria, while LF was endemic over most of Central- and Eastern Equatoria, but only in selected foci in Unity. All areas identified as STH endemic were co-endemic for schistosomiasis and/or LF. CONCLUSIONS: The distribution and prevalence of major NTDs, particularly schistosomiasis, varies considerably throughout South Sudan. Rapid mapping is therefore important in identifying (co)-endemic areas. The present survey established that across the three surveyed states between 1.2 and 1.4 million individuals are estimated to be eligible for regular MDA with PCT to treat STH and schistosomiasis, respectively, while approximately 1.3 million individuals residing in Central- and Eastern Equatoria are estimated to require MDA for LF.

Spatial prediction of malaria prevalence in an endemic area of Bangladesh.

BACKGROUND: Malaria is a major public health burden in Southeastern Bangladesh, particularly in the Chittagong Hill Tracts region. Malaria is endemic in 13 districts of Bangladesh and the highest prevalence occurs in Khagrachari (15.47%). METHODS: A risk map was developed and geographic risk factors identified using a Bayesian approach. The Bayesian geostatistical model was developed from previously identified individual and environmental covariates (p < 0.2; age, different forest types, elevation and economic status) for malaria prevalence using WinBUGS 1.4. Spatial correlation was estimated within a Bayesian framework based on a geostatistical model. The infection status (positives and negatives) was modeled using a Bernoulli distribution. Maps of the posterior distributions of predicted prevalence were developed in geographic information system (GIS). RESULTS: Predicted high prevalence areas were located along the north-eastern areas, and central part of the study area. Low to moderate prevalence areas were predicted in the southwestern, southeastern and central regions. Individual age and nearness to fragmented forest were associated with malaria prevalence after adjusting the spatial auto-correlation. CONCLUSION: A Bayesian analytical approach using multiple enabling technologies (geographic information systems, global positioning systems, and remote sensing) provide a strategy to characterize spatial heterogeneity in malaria risk at a fine scale. Even in the most hyper endemic region of Bangladesh there is substantial spatial heterogeneity in risk. Areas that are predicted to be at high risk, based on the environment but that have not been reached by surveys are identified.