Impact of truck contamination and information sharing on foot-and-mouth disease spreading in beef cattle production systems

• Published in: PloS one 2020-10, Vol.15 (10), p.e0240819-e0240819
• Main Authors: Yang, Qihui, Gruenbacher, Don M, Heier Stamm, Jessica L, Amrine, David E, Brase, Gary L, DeLoach, Scott A, Scoglio, Caterina M
• Format: Article
• Language: English
• Subjects: Animals; Beef; Beef cattle; Biology and Life Sciences; Cattle; Cattle Diseases - epidemiology; Cattle Diseases - pathology; Cattle Diseases - transmission; Cattle industry; Cattle production; Computer and Information Sciences; Computer engineering; Computer Simulation; Contamination; Disease control; Disease Outbreaks - veterinary; Disease spread; Disease transmission; Diseases; Distribution; Epidemic models; Epidemics; Foot & mouth disease; Foot and mouth disease; Foot-and-Mouth Disease - epidemiology; Foot-and-Mouth Disease - pathology; Foot-and-Mouth Disease - transmission; Funding; Information Dissemination; Information sharing; Mathematical models; Medicine and Health Sciences; Mitigation; Models, Biological; Motor Vehicles; Parameter estimation; Parameter sensitivity; Physical Sciences; Research and Analysis Methods; Sensitivity analysis; Simulation; Supply chains; Surveys and Questionnaires; Trade; Transportation; Trucks; Unloading
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0240819

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can effectively mitigate epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of the contamination period for a truck or a loading/unloading area of premises, and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.