Geospatial Health

Abstract

This paper aims to construct a spatially-explicit model of Ixodes scapularis infection in the State of New York, USA, based on climate traits, high-resolution landscape features and patch-connectivity according to graph theory. The degree of risk for infection is calculated based on empirical data of host abundance, previous studies on host infectivity rates and tick preferences towards a given host. The outcome signifies what is called the “recruitment of infection”, i.e. an index representing the abundance of infected ticks in a particular patch of vegetation. The results show that the I. scapularis recruitment of infection (IR) index is highly dependent upon a complex array of landscape fragmentation and the presence of key hosts. Neither faunal richness nor host density alone has any reasonable effect on the recruitment of infection. The experience of Lyme disease in the State of New York shows no clear relationship between the IR as calculated at the patch level and then summarized county by county and the rates of disease over the last eight years reported for these counties. However, areas characterized by low IR have consistently been associated with locations with low disease rates. Above all, the low levels of disease are related to minimal suitability due to the climate and negligible connection between patches. Social factors, mainly activities leading to an increased contact of humans with infective foci (which can be situated far from their homes), may lead to a high rates being reported from areas with high human densities rather than areas characterized by a high recruitment of infection. The spatial model developed here may be used to study the long-term changes in infective risk and tick recruitment as a result of humaninduced changes in the landscape.

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Cite this paper

@inproceedings{EstradaPea2009GeospatialH, title={Geospatial Health}, author={Agust{\'i}n Estrada-Pe{\~n}a}, year={2009} }