Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

Howard H Chang,Yang Liu,Alyson Lorenz,Radhika Dhingra,Justin V Remais,Donal Bisanzio

doi:10.1371/journal.pone.0103163

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.

Highlights

A range of human and ecological risk assessment activities involve applying quantitative knowledge—such as a model and its parameters drawn from previous work—to a new research question or analytical problem, or to a new geographic region or time period
These are drawn from previous work unrelated to environmental change, and this is especially true for relationships between landscape characteristics and infectious disease vectors, hosts, and reservoirs
Ixodes scapularis Models The large number of geographically limited landscape models for I. scapularis, the primary Lyme disease vector in the Eastern U.S, presents an opportunity to apply the checklist as summarized in Table 2, and examine how results from extrapolation differ across multiple models

Summary

Introduction

A range of human and ecological risk assessment activities involve applying quantitative knowledge—such as a model and its parameters drawn from previous work—to a new research question or analytical problem (conceptual extrapolation), or to a new geographic region or time period (spatial or temporal extrapolation). Examples of quantitative information routinely drawn from previous work include mathematical models and their parameters, dose-response functions, and thresholds and other parameter estimates [1,3] Common applications of such information include health impact assessments [4,5], ecological risk assessments [6,7], and risk mapping of disease vectors [8,9]. With growing interest in quantifying shifts in the spatial distribution of hazards, such as disease vector populations, in response to environmental change, models and their associated parameters that describe the environmental dependence of hazards are needed [10,11,12,13]. Predictions of future distributions of vectors, for instance, can aid in identifying areas to target for future funding and intervention [17]

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Conclusion