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SummaryBeginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.

Schistosomiasis is a water-based, infectious disease with high morbidity and significant economic burdens affecting >250 million people globally. Disease control has, with notable success, for decades focused on drug treatment of infected human populations, but a recent paradigm shift now entails moving from control to elimination. To achieve this ambitious goal, more sensitive diagnostic tools are needed to monitor progress toward transmission interruption in the environment, especially in low-intensity infection areas. We report on the development of an environmental DNA (eDNA)-based tool to efficiently detect DNA traces of the parasite Schistosoma mansoni directly in the aquatic environment, where the nonhuman part of the parasite life cycle occurs. This is a report of the successful detection of S. mansoni in freshwater samples by using aquatic eDNA. True eDNA was detected in as few as 10 cercariae per liter of water in laboratory experiments. The field applicability of the method was tested at known transmission sites in Kenya, where comparison of schistosome detection by conventional snail surveys (snail collection and cercariae shedding) with eDNA (water samples) showed 71% agreement between the methods. The eDNA method furthermore detected schistosome presence at two additional sites where snail shedding failed, demonstrating a higher sensitivity of eDNA sampling. We conclude that eDNA provides a promising tool to substantially improve the environmental surveillance of S. mansoni. Given the proper method and guideline development, eDNA could become an essential future component of the schistosomiasis control tool box needed to achieve the goal of elimination.

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BackgroundAfter many years of general neglect, interest has grown and efforts came under way for the mapping, control, surveillance, and eventual elimination of neglected tropical diseases (NTDs). Disease risk estimates are a key feature to target control interventions, and serve as a benchmark for monitoring and evaluation. What is currently missing is a georeferenced global database for NTDs providing open-access to the available survey data that is constantly updated and can be utilized by researchers and disease control managers to support other relevant stakeholders. We describe the steps taken toward the development of such a database that can be employed for spatial disease risk modeling and control of NTDs.MethodologyWith an emphasis on schistosomiasis in Africa, we systematically searched the literature (peer-reviewed journals and ‘grey literature’), contacted Ministries of Health and research institutions in schistosomiasis-endemic countries for location-specific prevalence data and survey details (e.g., study population, year of survey and diagnostic techniques). The data were extracted, georeferenced, and stored in a MySQL database with a web interface allowing free database access and data management.Principal FindingsAt the beginning of 2011, our database contained more than 12,000 georeferenced schistosomiasis survey locations from 35 African countries available under http://www.gntd.org. Currently, the database is expanded to a global repository, including a host of other NTDs, e.g. soil-transmitted helminthiasis and leishmaniasis.ConclusionsAn open-access, spatially explicit NTD database offers unique opportunities for disease risk modeling, targeting control interventions, disease monitoring, and surveillance. Moreover, it allows for detailed geostatistical analyses of disease distribution in space and time. With an initial focus on schistosomiasis in Africa, we demonstrate the proof-of-concept that the establishment and running of a global NTD database is feasible and should be expanded without delay.

Abstract. Geographic information system (GIS)-based modeling of an intermediate host snail species' environmental requirements using known occurrence records can provide estimates of its spatial distribution. When other data are lacking, this can be used as a rough spatial prediction of potential snail-borne disease transmission areas. Furthermore, knowledge of abiotic factors affecting intra-molluscan parasitic development can be used to make "masks" based on remotely sensed climatic data, and these can in turn be used to refine these predictions. We used data from a recent freshwater snail survey from Uganda, environmental data and the genetic algorithm for rule-set prediction (GARP) to map the potential distribution of snail species known to act as intermediate hosts of several human and animal parasites. The results suggest that large areas of Uganda are suitable habitats for many of these snail species, indicating a large potential for disease transmission. The lack of parasitological data still makes it difficult to determine the magnitude of actual disease transmission, but the predicted snail distributions might be used as indicators of potential present and future risk areas. Some of the predicted snail distribution maps were furthermore combined with temperature masks delineating suitable temperature regimes of the parasites they host. This revealed the coinciding suitable areas for snail and parasite, but also areas suitable for host snails, but apparently not for the parasites. Assuming that the developed models correctly reflect areas suitable for transmission, the applied approach could prove useful for targeting control interventions.

BackgroundIn Uganda, malaria and lymphatic filariasis (causative agent Wuchereria bancrofti) are transmitted by the same vector species of Anopheles mosquitoes, and thus are likely to share common environmental risk factors and overlap in geographical space. In a comprehensive nationwide survey in 2000-2003 the geographical distribution of W. bancrofti was assessed by screening school-aged children for circulating filarial antigens (CFA). Concurrently, blood smears were examined for malaria parasites. In this study, the resultant malariological data are analysed for the first time and the CFA data re-analysed in order to identify risk factors, produce age-stratified prevalence maps for each infection, and to define the geographical patterns of Plasmodium sp. and W. bancrofti co-endemicity.MethodsLogistic regression models were fitted separately for Plasmodium sp. and W. bancrofti within a Bayesian framework. Models contained covariates representing individual-level demographic effects, school-level environmental effects and location-based random effects. Several models were fitted assuming different random effects to allow for spatial structuring and to capture potential non-linearity in the malaria- and filariasis-environment relation. Model-based risk predictions at unobserved locations were obtained via Bayesian predictive distributions for the best fitting models. Maps of predicted hyper-endemic malaria and filariasis were furthermore overlaid in order to define areas of co-endemicity.ResultsPlasmodium sp. parasitaemia was found to be highly endemic in most of Uganda, with an overall population adjusted parasitaemia risk of 47.2% in the highest risk age-sex group (boys 5-9 years). High W. bancrofti prevalence was predicted for a much more confined area in northern Uganda, with an overall population adjusted infection risk of 7.2% in the highest risk age-group (14-19 year olds). Observed overall prevalence of individual co-infection was 1.1%, and the two infections overlap geographically with an estimated number of 212,975 children aged 5 - 9 years living in hyper-co-endemic transmission areas.ConclusionsThe empirical map of malaria parasitaemia risk for Uganda presented in this paper is the first based on coherent, national survey data, and can serve as a baseline to guide and evaluate the continuous implementation of control activities. Furthermore, geographical areas of overlap with hyper-endemic W. bancrofti transmission have been identified to help provide a better informed platform for integrated control.

Abstract. The rapidly growing field of three-dimensional software modeling of the Earth holds promise for applications in the geospatial health sciences. Easy-to-use, intuitive virtual globe technologies such as Google Earth™ enable scientists around the world to share their data and research results in a visually attractive and readily understandable fashion without the need for highly sophisticated geographical information systems (GIS) or much technical assistance. This paper discusses the utility of the rapid and simultaneous visualization of how the agents of parasitic diseases are distributed, as well as that of their vectors and/or intermediate hosts together with other spatially-explicit information. The resulting better understanding of the epidemiology of infectious diseases, and the multidimensional environment in which they occur, are highlighted. In particular, the value of Google Earth™, and its web-based pendant Google Maps™, are reviewed from a public health view point, combining results from literature searches and experiences gained thus far from a multidisciplinary project aimed at optimizing schistosomiasis control and transmission surveillance in sub-Saharan Africa. Although the basic analytical capabilities of virtual globe applications are limited, we conclude that they have considerable potential in the support and promotion of the geospatial health sciences as a userfriendly, straightforward GIS tool for the improvement of data collation, visualization and exploration. The potential of these systems for data sharing and broad dissemination of scientific research and results is emphasized.