Volume 10, Number 11—November 2004
ICEID & ICWID 2004
ICEID Session Summaries
Mathematical Modeling and Public Policy: Responding to Health Crises1
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Mathematical models have long been used to study complex biologic processes, such as the spread of infectious diseases through populations, but health policymakers have only recently begun using models to design optimal strategies for controlling outbreaks or to evaluate and possibly improve programs for preventing them. In this session, three examples of such models were examined.
Patient Dependency characterizes the epidemiology of disease transmission within multiple small wards with rapid patient turnover. Other variables affecting the epidemiology of resistance are the use of antimicrobial agents, introduction of colonized patients, and efficacy of infection-control measures. A Markov chain model originally made for vector-borne diseases was used to elucidate the relative importance of different routes within intensive care units.
State-of-the-art modeling approaches were used in Britain during the outbreak of 2001 to address such questions as: Were planned control policies sufficient to bring the epidemic under control? What was the optimal intensity of preemptive culling? Would a logistically feasible vaccination program be a more effective control option? This “real-time” use of models, although of help in devising an effective control strategy, also proved controversial.
Although models of infectious diseases have influenced public health policy in the United States, that process and its results could be improved by regular, direct contact and communication between modelers, policy advisors, and other infectious-disease experts. At the U.S. Department of Health and Human Services, the Secretary’s Council on Public Health Preparedness is sponsoring initiatives using various modeling approaches to assess biodefense strategies.
Common themes in this session were: 1) involving substantive experts, thereby ensuring that conceptual frameworks underlying the mathematics are faithful to current understanding of complex natural phenomena, 2) including all possible interventions, which could then be evaluated alone or in various combinations, and 3) identifying inadequacies in available information, for augmentation through further research.
Suggested citation for this article: Glasser J, Meltzer M, Levin B. Mathematical modeling and public policy: responding to health crises. Emerg Infect Dis [serial on the Internet]. 2004 Nov [date cited]. Available from http://wwwnc.cdc.gov/eid/article/10/11/04-0797_08
1Presenters: Marc Bonten, Utrecht University Medical Center; Mark Woolhouse, University of Edinburgh; and Ellis McKenzie, National Institutes of Health.
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