Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2’s pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.Expected final online publication date for the Annual Review of Public Health, Volume 45 is April 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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通过疾病负担、影响评估和经济评估模型为公共卫生政策提供信息

进行现实世界的公共卫生实验通常成本高昂、耗时且在道德上具有挑战性，因此数学模型被用来为政策提供信息有着悠久的历史。应用包括估计疾病负担、对干预措施进行经济评估以及应对流行病等突发卫生事件。模型在 COVID-19 大流行期间发挥了关键作用，可以及早发现 SARS-CoV-2 的大流行潜力，并为后续的公共卫生措施提供信息。虽然模型提供了有价值的政策见解，但它们通常具有局限性，特别是当它们依赖于假设和不完整的数据时。在疾病爆发等快速变化的情况下，在准确性和及时决策之间取得平衡具有挑战性。建模者需要探索他们的模型在多大程度上偏离了代表现实世界。模型固有的不确定性必须有效地传达给政策制定者和公众。随着该领域的影响力越来越大，需要制定能够接受严格外部审查的报告标准。《公共卫生年度评论》第 45 卷的预计最终在线发布日期为 2024 年 4 月。请参阅 http://www.annualreviews.org/修订后的估计的页面/期刊/出版物。