Singer Randall S, Williams-Nguyen Jessica
Department of Veterinary and Biomedical Sciences, University of Minnesota, 1971 Commonwealth Ave., St. Paul, MN 55108, USA; Instituto de Medicina Preventiva Veterinaria, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile.
Department of Veterinary and Biomedical Sciences, University of Minnesota, 1971 Commonwealth Ave., St. Paul, MN 55108, USA; Department of Epidemiology, School of Public Health, University of Washington, 1959 NE Pacific Street, Health Sciences Building F-262, Box 357236, Seattle, WA 98195-7236, USA.
Curr Opin Microbiol. 2014 Jun;19:1-8. doi: 10.1016/j.mib.2014.05.014. Epub 2014 Jun 17.
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.
人类中的耐药细菌感染在全球范围内仍然构成重大挑战。农业中抗生素的使用加剧了这一问题，但未能认识到各种潜在原因的相对重要性是有效干预的重大障碍。仅靠标准的流行病学方法往往不足以准确描述农业抗生素使用与耐药性之间的关系。整合来自多个学科的不同方法将至关重要，包括因果网络建模和种群动态方法。由于在指导这些非线性和相互关联系统的调查工作时，直觉可能是一个糟糕的指引，因此在一个迭代过程中将建模工作与实证流行病学和微生物学相结合，可能会产生比单独任何一方更有价值的信息。
