The COVID-19 pandemic created needs for (a) estimating the existing airborne risk of infection from SARS-CoV-2 in existing facilities and new designs and (b) estimating and comparing the impacts of engineering and behavioural strategies for contextually reducing that risk. This paper presents the development of a web application to meet these needs, the Facility Infection Risk Estimator™, and its underlying Wells–Riley based model. The model specifically estimates (a) the removal efficiencies of various settling, ventilation, filtration and virus inactivation strategies and (b) the associated probability of infection, given the room physical parameters and number of individuals infected present with either influenza or SARS-CoV-2. A review of the underlying calculations and associated literature is provided, along with the model's validation against two documented spreading events. The error between modelled and actual number of additional people infected, normalized by the number of uninfected people present, ranged from roughly –18.4% to +9.7%. The more certain one can be regarding the input parameters (such as for new designs or existing buildings with adequate field verification), the smaller these normalized errors will be, likely less than ±15%, making it useful for comparing the impacts of different risk mitigation strategies focused on airborne transmission.

COVID-19 大流行产生了以下需求：(a) 估计现有设施​​和新设计中 SARS-CoV-2 感染的现有空气传播风险，以及 (b) 估计和比较工程和行为策略的影响，以便在上下文中降低这种风险。本文介绍了满足这些需求的 Web 应用程序的开发、Facility Infection Risk Estimator™ 及其基于 Wells-Riley 的模型。该模型具体估计 (a) 各种沉降、通风、过滤和病毒灭活策略的去除效率和 (b) 相关感染概率，考虑到房间物理参数和感染流感或 SARS-CoV 的个体数量- 2. 提供了对基础计算和相关文献的回顾，以及模型 针对两个记录的传播事件进行验证。模拟的和实际感染人数之间的误差，通过在场未感染人数标准化，范围从大约 –18.4% 到 +9.7%。输入参数越确定（例如对于新设计或具有充分现场验证的现有建筑物），这些归一化误差就越小，可能小于 ±15%，从而有助于比较不同风险的影响缓解策略侧重于空气传播。