A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States.

Abstract

An understanding of the spatial distribution of the black-legged tick, Ixodes scapularis, is a fundamental component in assessing human risk for Lyme disease in much of the United States. Although a county-level vector distribution map exists for the United States, its accuracy is limited by arbitrary categories of its reported presence. It is unknown whether reported positive areas can support established populations and whether negative areas are suitable for established populations. The steadily increasing range of I. scapularis in the United States suggests that all suitable habitats are not currently occupied. Therefore, we developed a spatially predictive logistic model for I. scapularis in the 48 conterminous states to improve the previous vector distribution map. We used ground-observed environmental data to predict the probability of established I. scapularis populations. The autologistic analysis showed that maximum, minimum, and mean temperatures as well as vapor pressure significantly contribute to population maintenance with an accuracy of 95% (p < 0.0001). A cutoff probability for habitat suitability was assessed by sensitivity analysis and was used to reclassify the previous distribution map. The spatially modeled relationship between I. scapularis presence and large-scale environmental data provides a robust suitability model that reveals essential environmental determinants of habitat suitability, predicts emerging areas of Lyme disease risk, and generates the future pattern of I. scapularis across the United States.

Cited by papers we hold 4

• Climate change and Ixodes tick-borne diseases of humans · 2015
  No notice
• Research on the ecology of ticks and tick-borne pathogens—methodological principles and caveats · 2013
  No notice
• Risk maps for range expansion of the Lyme disease vector, Ixodes scapularis, in Canada now and with climate change · 2008
  No notice
• Free-grazing Ducks and Highly Pathogenic Avian Influenza, Thailand · 2006
  No notice

Reads

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.