Ticks and tick-borne disease systems in space and from space

Abstract

Analyses within geographical information systems (GISs) indicate that small- and large-scale ranges of hard tick species (Ixodidae) are determined more by climate and vegetation than by host-related factors. Spatial distributions of ticks may therefore be analysed by statistical methods that seek correlations between known tick presence/absence and ground- or remotely-sensed (RS) environmental factors. In this way, local habitats of Amblyomma variegatum in the Caribbean and Ixodes ricinus in Europe have been mapped using Landsat RS imagery, while regional and continental distributions of African and temperate tick species have been predicted using multi-temporal information from the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) imagery. These studies illustrate ways of maximizing statistical accuracy, whose interpretation is then discussed in a biological framework. Methods such as discriminant analysis are biologically transparent and interpretable, while others, such as logistic regression and tree-based classifications, are less so. Furthermore, the most consistently significant variable for predicting tick distributions, the RS Normalized Difference Vegetation Index (NDVI), has a sound biological basis in that it is related to moisture availability to free-living ticks and correlated with tick mortality rates. The development of biological process-based models for predicting the spatial dynamics of ticks is a top priority, especially as the risk of tick-borne infections is commonly related not simply to the vector's density, but to its seasonal population dynamics. Nevertheless, using statistical pattern-matching, the combination of RS temperature indices and NDVI successfully predicts certain temporal features essential for the transmission of tick-borne encephalitis virus, which translate into a spatial pattern of disease foci on a continental scale.