Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

Clements, Archie C. A.; Lwambo, N. J. S.; Blair, Lynsey; Nyandindi, Ursuline; Kaatano, Godfrey M.; Kinung’hi, Safari; Webster, Joanne P.; Fenwick, Alan; Brooker, Simon

doi:10.1111/j.1365-3156.2006.01594.x

Cited by 206 publications

(229 citation statements)

References 28 publications

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“…Future studies should preferentially test the datasets with the newer but rarely used species distribution modeling methods, including machine-learning methods and community models which recently have been shown to outperform more established methods (Elith et al, 2006). Furthermore, Bayesian geostatistical analysis, incorporating uncertainty into the modeling process, has recently proven a powerful and statistically robust tool for identifying high schistosomiasis prevalence areas in a heterogeneous and imperfectly known environment, an approach that enables objective decisions to be taken as to the need for further data collection (Raso et al, 2005;Clements et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

Stensgaard

Jørgensen²,

Kabatereine

et al. 2006

Geospat Health

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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.

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Section: Discussionmentioning

confidence: 99%

Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

Stensgaard

Jørgensen²,

Kabatereine

et al. 2006

Geospat Health

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“…Such model-based geostatistical approaches have recently been used to study the geographical distribution of tropical diseases both at larger or smaller scale including malaria [7]. These approaches were integrated to develop a malaria risk map for this highly endemic region of malaria.…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of malaria prevalence in an endemic area of Bangladesh

Haque

Magalhães

Reid

et al. 2010

Malar J

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BackgroundMalaria 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%).MethodsA 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).ResultsPredicted 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.ConclusionA 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.

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“…As a useful alternative, BG analyses invariably present summary maps ( Figure 3) [2,7,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Constructing these maps from the posterior is straightforward.…”

Section: 'The' Map and The Role Of Gismentioning

confidence: 99%

Bayesian geostatistics in health cartography: the perspective of malaria

Patil

Gething

Piel

et al. 2011

Trends in Parasitology

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Maps of parasite prevalences and other aspects of infectious diseases that vary in space are widely used in parasitology. However, spatial parasitological datasets rarely, if ever, have sufficient coverage to allow exact determination of such maps. Bayesian geostatistics (BG) is a method for finding a large sample of maps that can explain a dataset, in which maps that do a better job of explaining the data are more likely to be represented. This sample represents the knowledge that the analyst has gained from the data about the unknown true map. BG provides a conceptually simple way to convert these samples to predictions of features of the unknown map, for example regional averages. These predictions account for each map in the sample, yielding an appropriate level of predictive precision. The need for Bayesian geostatisticsA recently described, large database of Plasmodium falciparum endemicity surveys in Africa and Yemen [1,2] is shown in Figure 1 in two and three dimensions. The data are highly clustered and coverage is sparse in Central Africa. The short-range variation in the data is striking; for example, in the small cluster to the east of Lake Victoria, where observed prevalences range widely from zero to near 80%. Efforts to account for this variability by means of environmental factors [3][4][5], time [2], and age [6] are ongoing, but much of it remains unexplained, possibly because local variation in environment and human activities are not captured by the environmental data available at continental scales.These observations call into question the usefulness of producing a single map of P. falciparum endemicity in Africa and elsewhere. A map provides a single estimate for each location in the mapped region, and it is clearly impossible to make accurate and precise estimates of parasite rates in Central Africa based on the sparse data coverage there. Even if data coverage were uniformly dense, the unexplained short-range variability evident in datarich East Africa indicates that no single value would capture the wide range of endemicities that might be encountered at an unsampled location. The malaria epidemiology community faces the problem of converting this patchy dataset, with substantial unexplained variation, into advice for a range of users.Bayesian geostatistics (BG), which is becoming the standard mapping technique in certain branches of parasitology [7], is well suited to generating advice under uncertainty because it attempts to find a large sample of maps that are consistent with the dataset rather than a single map. This sample is encapsulated in the posterior distribution. This opinion is a guide Posterior distributionsA posterior distribution, often called a posterior, is a probability distribution that has been informed by data according to the rules of Bayesian inference [8]. Figure 2 shows a probability distribution for a random number labeled X. In the Bayesian interpretation of probability, 'random' just means 'unknown;' random numbers such as X are understood to have unkn...

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Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania

Cited by 206 publications

References 28 publications

Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

Spatial prediction of malaria prevalence in an endemic area of Bangladesh

Bayesian geostatistics in health cartography: the perspective of malaria

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