Mapping HAE disease risk using remotely sensed data

Human alveolar echinococcosis (HAE) is a fatal parasitic disease occurring in humans infected with the larval stage of the fox tapeworm Echinococcus multilocularis. Pre-symptomatic detection can be a critical factor in the treatment success of HAE. There is a need then to map infection risk to facilitate early detection of HAE cases. Microtine rodents are critical intermediate hosts in the sustainable transmission of E. multilocularis. Maximum likelihood classification of remote sensing imagery can provide a basis for quantitative microtine habitat analysis leading to significant improvements to HAE risk models. Two examples of this landscape ecology approach to HAE risk modeling are presented here. In the first example CORINE data is shown to improve geostatistical prediction of the infection status in foxes. In the second example Landsat TM and MSS data are shown to improve a model of human infection. It is concluded that remote sensing data, image classification, geographical information systems (GIS) and geostatistics provide useful epidemiological tools for the prediction of HAE hotspots.