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Improving Decision Support for Infectious Disease Prevention and Control: Aligning Models and Other Tools with Policymakers' Needs


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Infectious disease outbreaks present a challenge to nations and the interconnected global community. As evidenced by the outbreaks of severe acute respiratory syndrome (SARS) in 2003, influenza in 2009, Ebola and Middle East respiratory syndrome (MERS) in 2014, and the Zika virus in 2016, infectious diseases can spread rapidly within countries and across national borders. Policymakers are responsible for decisions about the nature and timing of appropriate courses of action to prevent, detect, and respond to an infectious disease outbreak. They need answers to questions about when, where, how fast, and how widely disease will spread; about the availability, effectiveness, cost, and potential unintended consequences of interventions; and about how to assess performance during and after an outbreak response. The policymakers decisions must be made at different points as an outbreak emerges and spreads and with varying degrees of warning or available data to support those decisions. Evidence-based decision support can play a crucial role in informing these decisions, even taking critical uncertainties into account. However, modeling for decision support has not always been entirely satisfying. For example, many analyses of the 2009 influenza pandemic, as well as other outbreaks, have revealed failures to use models properly. Although there has been notable success and progress since then, further work is neededfor example, to clearly define expectations and to improve coordination among modelers and between modelers and clinicians, epidemiologists, and policymakers.



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