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

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