Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
By: , and 

Links

Abstract

Managing pathogen spillover at the wildlife–livestock interface is a key step towards improving global animal health, food security and wildlife conservation. However, predicting the effectiveness of management actions across host–pathogen systems with different life histories is an on-going challenge since data on intervention effectiveness are expensive to collect and results are system-specific. We developed a simulation model to explore how the efficacies of different management strategies vary according to host movement patterns and epidemic growth rates. The model suggested that fast-growing, fast-moving epidemics like avian influenza were best-managed with actions like biosecurity or containment, which limited and localized overall spillover risk. For fast-growing, slower-moving diseases like foot-and-mouth disease, depopulation or prophylactic vaccination were competitive management options. Many actions performed competitively when epidemics grew slowly and host movements were limited, and how management efficacy related to epidemic growth rate or host movement propensity depended on what objective was used to evaluate management performance. This framework offers one means of classifying and prioritizing responses to novel pathogen spillover threats, and evaluating current management actions for pathogens emerging at the wildlife–livestock interface.

Publication type Article
Publication Subtype Journal Article
Title Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface
Series title Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
DOI 10.1098/rstb.2018.0343
Volume 374
Issue 1782
Year Published 2019
Language English
Publisher The Royal Society
Contributing office(s) Northern Rocky Mountain Science Center
Description 20180343
Google Analytic Metrics Metrics page
Additional publication details