Development of a stochastic agent-based model to evaluate surveillance strategies for detection of emergent porcine reproductive and respiratory syndrome strains

BMC Veterinary Research

Research Article

D. Knowles¹  A. McLean¹  Z. Poljak²  A. G. Arruda² 

[1] Department of Computer Science, Computational Epidemiology and Public Health Informatics Lab, University of Saskatchewan, 176 Thorvaldson Bldg, 110 Science Place, S7N 5C9, Saskatoon, SK, Canada;Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Rd East, N1G 2W1, Guelph, ON, Canada;

关键词: Stochastic agent-based model;    Porcine reproductive and respiratory syndrome control;    Porcine reproductive and respiratory syndrome surveillance;    Risk-based surveillance;    Surveillance system sensitivity;   

DOI  :  10.1186/s12917-017-1091-7

 received in 2015-12-03, accepted in 2017-06-05,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe objective of the current study was to develop a stochastic agent-based model using empirical data from Ontario (Canada) swine sites in order to evaluate different surveillance strategies for detection of emerging porcine reproductive and respiratory syndrome virus (PRRSV) strains at the regional level. Four strategies were evaluated, including (i) random sampling of fixed numbers of swine sites monthly; (ii) risk-based sampling of fixed numbers, specifically of breeding sites (high-consequence sites); (iii) risk-based sampling of fixed numbers of low biosecurity sites (high-risk); and (iv) risk-based sampling of breeding sites that are characterized as low biosecurity sites (high-risk/high-consequence). The model simulated transmission of a hypothetical emerging PRRSV strain between swine sites through three important industry networks (production system, truck and feed networks) while considering sites’ underlying immunity due to past or recent exposure to heterologous PRRSV strains, as well as demographic, geographic and biosecurity-related PRRS risk factors. Outcomes of interest included surveillance system sensitivity and time to detection of the three first cases over a period of approximately three years.ResultsSurveillance system sensitivities were low and time to detection of three first cases was long across all examined scenarios.ConclusionTraditional modes of implementing high-risk and high-consequence risk-based surveillance based on site’s static characteristics do not appear to substantially improve surveillance system sensitivity. Novel strategies need to be developed and considered for rapid detection of this and other emerging swine infectious diseases. None of the four strategies compared herein appeared optimal for early detection of an emerging PPRSV strain at the regional level considering model assumptions, the underlying population of interest, and absence of other forms of surveillance.

【 授权许可】

CC BY   
© The Author(s). 2017