Inter-Model Comparison of the Landscape Determinants of Vector-Borne Disease: Implications for Epidemiological and Entomological Risk Modeling

• Type of Material: Article
• Authors: Alyson Lorenz, Emory University; Radhika Dhingra, Emory University; Howard Chang, Emory University; Donal Bisanzio, Emory University; Yang Liu, Emory University; Justin V Remais, Emory University
• Language: English
• Date: 2014
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Lorenz et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0103163
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 9
• Issue: 7
• Start Page: e103163
• End Page: e103163
• Grant/Funding Information: This work was supported in part by the CDC Climate and Health Program (award # 5 U01 EH000405), the Ecology of Infectious Disease program of the National Science Foundation under Grant No. 0622743, the National Institute for Allergy and Infectious Disease (K01AI091864) and the Global Health Institute at Emory University.
• Research Categories: Environmental Sciences; Health Sciences, Public Health

Abstract

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.