Harvard University, Cambridge, MA, USA.
Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA.
Med Decis Making. 2021 May;41(4):379-385. doi: 10.1177/0272989X21990391. Epub 2021 Feb 3.
Mathematical modeling has played a prominent and necessary role in the current coronavirus disease 2019 (COVID-19) pandemic, with an increasing number of models being developed to track and project the spread of the disease, as well as major decisions being made based on the results of these studies. A proliferation of models, often diverging widely in their projections, has been accompanied by criticism of the validity of modeled analyses and uncertainty as to when and to what extent results can be trusted. Drawing on examples from COVID-19 and other infectious diseases of global importance, we review key limitations of mathematical modeling as a tool for interpreting empirical data and informing individual and public decision making. We present several approaches that have been used to strengthen the validity of inferences drawn from these analyses, approaches that will enable better decision making in the current COVID-19 crisis and beyond.
数学建模在当前的 2019 年冠状病毒病(COVID-19)大流行中发挥了突出而必要的作用,越来越多的模型被开发出来以跟踪和预测疾病的传播,并且根据这些研究的结果做出了重大决策。模型的大量涌现,其预测结果往往存在很大差异,这引发了对模型分析有效性的批评,以及对何时以及在何种程度上可以信任结果的不确定性。我们从 COVID-19 和其他具有全球重要性的传染病中选取了一些例子,回顾了数学建模作为解释经验数据和为个人和公共决策提供信息的工具的主要局限性。我们提出了几种已被用于加强从这些分析中得出的推论的有效性的方法,这些方法将使我们能够在当前的 COVID-19 危机及以后做出更好的决策。