Trends in Parasitology
Volume 18, Issue 2, 1 February 2002, Pages 70-74
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Tools from ecology: useful for evaluating infection risk models?

https://doi.org/10.1016/S1471-4922(01)02223-1Get rights and content

Abstract

Despite the increasing number of models to predict infection risk for a range of diseases, the assessment of their spatial limits, predictive performance and practical application are not widely undertaken. Using the example of Schistosoma haematobium in Africa, this article illustrates how ecozonation and receiver–operator characteristic analysis can help to assess the usefulness of available models objectively.

Section snippets

Developing predictive models

Reliable maps of infectious diseases require an understanding of whether models developed for one location can be applied to another because the environmental factors that influence disease transmission are unlikely to be uniform over large geographical areas [5]. Political boundaries are routinely used to define the spatial extent of risk maps, but the ecological heterogeneity, which is usually independent of political boundaries, is ignored. Alternatively, remotely-sensed (RS) derived

Schistosomiasis

In common with other infectious diseases, the design of schistosomiasis control operations in Africa is typically constrained by a lack of comprehensive survey data [11]. Climate and other environmental variables influences the distribution of schistosomiasis (either intestinal Schistosoma mansoni or urinary Schistosoma haematobium) at broad spatial scales [12], hence RS-derived environmental data have potential for predicting transmission patterns [13]. Such an approach has been used to map

Do the models perform reliably in other areas?

The real test of a model lies in applying it to different locations. We therefore validated predictions from the model developed in the Tanga Region by using independent data from the Magu, Kilosa, Mtwara and Tandahimba districts of Tanzania (Fig. 1). The Cameroon model was also validated using all the data from Tanzania. ROC analysis indicated that within Tanzania, the model developed for Tanga Region performed reasonably well in neighboring Kilosa District and further south in the

Control applications

The risk maps developed will be unable to capture the well-known foci of schistosomiasis: heterogeneities in water contact patterns [25], and the genetic diversity of S. haematobium [23] will also influence patterns on a local scale. The large-area RS-based models, however, are useful for identifying potential areas of high risk (Fig. 2). In particular, these maps can exclude areas where urinary schistosomiasis is unlikely to be prevalent, and so help focus on priority areas where local

Concluding remarks

Prediction models are increasingly being developed for a variety of infectious diseases. Validation of such models is a difficult but an essential issue [9] if models are to have practical relevance for control. ROC plots are a useful addition to logistic regressions procedures for disease risk mapping by optimizing the choice of probability threshold required to satisfy stated control objectives. Ecological zone maps are also of value in defining the spatial extent to which such models can be

Acknowledgements

We thank David Rogers and William Wint for valuable discussions on the concept of ecological zoning and its potential application in epidemiology. Raoult Ratard, Joanne Webster, Andrew Hall, Andrew Roddam, Nicholas Lwambo and members of the Tanzania Partnership for Child Development (PCD) are also thanked for their input. The survey work in Cameroon was funded by USAID and the survey work in Tanzania was funded by the PCD, the Edna McConnell Clark Foundation and The Wellcome Trust. S.B. and

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