Resistant bacterial infections in humans continue to pose a significant challenge globally. Antibiotic use in agriculture contributes to this problem, but failing to appreciate the relative importance of diverse potential causes represents a significant barrier to effective intervention. Standard epidemiologic methods alone are often insufficient to accurately describe the relationships between agricultural antibiotic use and resistance. The integration of diverse methodologies from multiple disciplines will be essential, including causal network modeling and population dynamics approaches. Because intuition can be a poor guide in directing investigative efforts of these non-linear and interconnected systems, integration of modeling efforts with empirical epidemiology and microbiology in an iterative process may result in more valuable information than either in isolation.
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We thank Dörte Döpfer, Tim Johnson and Kevin Lang for input on the manuscript. This material is based, in part, on work supported by the Cooperative State Research Service, U.S. Department of Agriculture, under Project No. MIN-63-017.