title: Progression to multi-scale models and the application to food system intervention strategies.
abstract: The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner. The examples are drawn from our current research. The first, explained in depth, uses clinical mastitis to introduce the concept of dynamic programming to optimize management decisions in dairy cows (also introducing the curse of dimensionality problem). In the second example, a compartmental epidemic model for Johne's disease with different intervention strategies is optimized. The goal of the optimization strategy depends on whether there is a relationship between Johne's and Crohn's disease. If so, optimization is based on eradication of infection; if not, it is based on the cow's performance only (i.e., economic optimization, similar to the mastitis example). The third example focuses on food safety to introduce risk assessment using Listeria monocytogenes and Salmonella Typhimurium. The last example, practical interventions to effectively manage antibiotic resistance in beef and dairy cattle systems, introduces meta-population modeling that accounts for bacterial growth not only in the host (cow), but also in the cow's feed, drinking water and the housing environment. Each example stresses the need to progress toward multi-scale modeling. The article ends with examples of multi-scale systems, from food supply systems to Johne's disease. Reducing the consequences of foodborne illnesses (i.e., minimizing disease occurrence and associated costs) can only occur through an understanding of the system as a whole, including all its complexities. Thus the goal of future research should be to merge disciplines such as molecular biology, applied mathematics and social sciences to gain a better understanding of complex systems such as the food supply chain.
authors.affiliation: Section of Epidemiology, Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, S3-108 Schurman Hall, Ithaca, NY 14853, USA. Electronic address: ytg1@cornell.edu.
authors.forename: Yrjö T
authors.initials: YT
authors.lastname: Gröhn
mesh_terms: D000818:Animals; D002417:Cattle; D002418:Cattle Diseases; D003362:Cost-Benefit Analysis; D003612:Dairying; D004352:Drug Resistance, Microbial; D005260:Female; D005516:Food Microbiology / Q000191:economics; D059022:Food Safety; D005523:Food Supply; D006801:Humans; D008089:Listeria monocytogenes; D008137:Longitudinal Studies; D008414:Mastitis, Bovine / Q000191:economics; D008954:Models, Biological; D009518:New York / Q000453:epidemiology; D010283:Paratuberculosis / Q000453:epidemiology; D018570:Risk Assessment; D012486:Salmonella typhimurium; D018511:Systems Integration
keywords: Dynamic programming; Food supply system; Infectious disease modeling; Multi-scale modeling; Risk assessment; System science