Department of Mathematics and Statistics, Washington University in St. Louis, Saint Louis, Missouri, United States of America.
Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States of America.
PLoS One. 2021 Jan 28;16(1):e0245381. doi: 10.1371/journal.pone.0245381. eCollection 2021.
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 model of SARS-CoV-2 infection probability that can produce estimates of relative risk of infection for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day (e.g., sporting events, flights, concerts), and that the probability of infection among possible routes of infection (i.e., droplet, aerosol, fomite, and direct contact) are independent. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes; in particular, it allows for estimating the ranking of the constituent components of activities (e.g., going through a turnstile, sitting in one's seat) by their relative risk of infection, even when the probability of infection is unknown or uncertain. 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. A linearity assumption governing several potentially modifiable risks factors-such as duration of the activity, density of participants, and infectiousness of the attendees-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方案、相关结果，并对不确定性进行量化。围绕 COVID-19 大流行的公共和私人决策必须应对与人群活动期间感染概率相关的不确定性，以确定该活动是否值得开展。我们提出了一种 SARS-CoV-2 感染概率模型，只要符合一系列假设，包括活动持续时间不超过一天（如体育赛事、航班、音乐会），以及感染的可能途径（即飞沫、气溶胶、污染物和直接接触）之间的感染概率相互独立，就可以为各种活动产生感染相对风险的估计值。我们展示了如何使用该模型为政府和行业面临的决策提供信息，例如开放体育场或乘坐飞机；特别是，它允许根据感染的相对风险来估计活动组成部分（例如，通过检票口、坐在座位上）的排名，即使感染的概率未知或不确定。我们证明该模型是对更精细模型的良好近似，其中我们假设感染来自一系列独立的风险。线性假设可以控制几个潜在可修改的风险因素，如活动持续时间、参与者密度和参与者的传染性，使得解释和使用模型变得简单，我们认为，这并不会显著降低模型的可靠性。
