tetano
Editor, Senior Moderator
Philos Trans R Soc Lond B Biol Sci. 2019 Sep 30;374(1782):20180343. doi: 10.1098/rstb.2018.0343. Epub 2019 Aug 12.
[h=1]Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface.[/h] Manlove KR[SUP]1[/SUP], Sampson LM[SUP]2[/SUP], Borremans B[SUP]3,[/SUP][SUP]4[/SUP], Cassirer EF[SUP]5[/SUP], Miller RS[SUP]6[/SUP], Pepin KM[SUP]7[/SUP], Besser TE[SUP]8[/SUP], Cross PC[SUP]9[/SUP].
[h=3]Author information[/h] 1 Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT 84321, USA. 2 Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA 16802, USA. 3 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095-7239, USA. 4 Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT), Hasselt University, 3590 Diepenbeek, Belgium. 5 Idaho Department of Fish and Game, 3316 16th Street, Lewiston, ID 83501, USA. 6 United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, CO 80523, USA. 7 National Wildlife Research Center, USDA-APHIS, Wildlife Services, 4101 Laporte Ave., Fort Collins, CO 80521, USA. 8 Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA 99164-7040, USA. 9 U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT 59715, USA.
[h=3]Abstract[/h] 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. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
[h=4]KEYWORDS:[/h] disease management; disease model; dispersal kernel; pathogen spillover; structured decision-making; wildlife?livestock interface
PMID: 31401952 DOI: 10.1098/rstb.2018.0343
[h=1]Epidemic growth rates and host movement patterns shape management performance for pathogen spillover at the wildlife-livestock interface.[/h] Manlove KR[SUP]1[/SUP], Sampson LM[SUP]2[/SUP], Borremans B[SUP]3,[/SUP][SUP]4[/SUP], Cassirer EF[SUP]5[/SUP], Miller RS[SUP]6[/SUP], Pepin KM[SUP]7[/SUP], Besser TE[SUP]8[/SUP], Cross PC[SUP]9[/SUP].
[h=3]Author information[/h] 1 Department of Wildland Resources and Ecology Center, Utah State University, Logan, UT 84321, USA. 2 Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA 16802, USA. 3 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095-7239, USA. 4 Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BIOSTAT), Hasselt University, 3590 Diepenbeek, Belgium. 5 Idaho Department of Fish and Game, 3316 16th Street, Lewiston, ID 83501, USA. 6 United States Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Fort Collins, CO 80523, USA. 7 National Wildlife Research Center, USDA-APHIS, Wildlife Services, 4101 Laporte Ave., Fort Collins, CO 80521, USA. 8 Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA 99164-7040, USA. 9 U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT 59715, USA.
[h=3]Abstract[/h] 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. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.
[h=4]KEYWORDS:[/h] disease management; disease model; dispersal kernel; pathogen spillover; structured decision-making; wildlife?livestock interface
PMID: 31401952 DOI: 10.1098/rstb.2018.0343