McCarthy John E, Dumas Bob A, McCarthy Myles T, Dewitt Barry D
Department of Mathematics and Statistics, Washington University in St. Louis.
Omnium LLC.
medRxiv. 2020 Aug 25:2020.08.23.20180349. doi: 10.1101/2020.08.23.20180349.
Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a deterministic linear model of SARS-CoV-2 infection probability that can produce estimates of relative risk for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. The linearity assumption makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.
风险-成本-效益分析需要列举决策选项、其相关结果以及不确定性的量化。围绕新冠疫情的公共和私人决策必须应对在涉及人群的活动中感染概率的不确定性，以便决定该活动是否值得开展。我们提出了一种新冠病毒感染概率的确定性线性模型，只要这些活动满足一系列假设，包括持续时间不超过一天，该模型就能对各种活动的相对风险进行估计。我们展示了该模型如何用于为政府和行业面临的决策提供信息，比如开放体育场或乘坐飞机。我们证明，在假设感染来自一系列独立风险的更精细模型中，该模型是一个很好的近似。线性假设使模型的解释和使用变得直接明了，我们认为这样做不会显著降低模型的可靠性。
