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

Authors

Kezia R. Manlove, Utah State UniversityFollow
Laura M. Sampson, The Pennsylvania State University
Benny Borremans, University of California & Hasselt University
E. Frances Cassirer, Idaho Department of Fish and Game
Ryan S. Miller, Center for Epidemiology and Animal HealthFollow
Kim M. Pepin, USDA-APHIS, Wildlife ServicesFollow
Thomas E. Besser, Washington State University
Paul C. Cross, U.S. Geological Survey

ORCID IDs

http://orcid.org/0000-0002-7200-5236

http://orcid.org/0000-0001-8651-9302

http://orcid.org/0000-0002-7779-4107

http://orcid.org/0000-0002-9931-8312

http://orcid.org/0000-0003-0449-1989

http://orcid.org/0000-0001-8045-5213

Document Type

Article

Date of this Version

5-13-2019

Citation

Manlove, K.R., L.M. Sampson, B. Borremans, E.F. Cassirer, R.S. Miller, K.M. Pepin, T.E. Besser, and P.C. Cross. 2019. Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface. Philosophical Transactions of the Royal Society B 374(1782):20180343. doi: 10.1098/rstb.2018.0343

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 objectivewas used to evaluatemanagement 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. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.