Retrospective analyses of interventions to epidemics, in which the effectiveness of strategies implemented are compared to hypothetical alternatives, are valuable for performing the cost–benefit calculations necessary to optimize infection countermeasures. SIR (susceptible–infected–removed) models are useful in this regard but are limited by the challenge of deciding how and when to update the numerous parameters as the epidemic changes in response to population behaviors. Behaviors of particular interest include facemask adoption (at various levels) and social distancing. We present a method that uses a “dynamic spread function” to systematically capture the continuous variation in the population behavior and the gradual change in infection evolution, resulting from interventions. No parameter updates are made by the user. We use the tool to quantify the reduction in infection rate realizable from the population of New York City adopting different facemask strategies during COVID-19. Assuming a baseline facemask of 67% filtration efficiency, calculations show that increasing the efficiency to 80% could have reduced the roughly 5000 new infections per day occurring at the peak of the epidemic to around 4000. Population behavior that may not be varied as part of the retrospective analysis, such as social distancing in a facemask analysis, are automatically captured as part of the calibration of the dynamic spread function.

对流行病干预措施的回顾性分析，将所实施策略的有效性与假设的替代方案进行比较，对于进行优化感染对策所需的成本效益计算很有价值。 SIR（易感-感染-移除）模型在这方面很有用，但受到随着流行病因人群行为而变化而决定如何以及何时更新众多参数的挑战的限制。特别令人感兴趣的行为包括戴口罩（在不同程度上）和保持社交距离。我们提出了一种方法，使用“动态传播函数”来系统地捕捉干预措施导致的人群行为的持续变化和感染演变的逐渐变化。用户不进行任何参数更新。我们使用该工具来量化纽约市人口在 COVID-19 期间采用不同口罩策略可实现的感染率降低。假设基准口罩的过滤效率为 67%，计算表明，将效率提高到 80% 可以将疫情高峰期每天发生的约 5000 例新感染病例减少到 4000 例左右。回顾性分析，例如口罩分析中的社交距离，会自动捕获，作为动态传播函数校准的一部分。