Engineer Research and Development Center, Vicksburg, MS, USA.
Carnegie Mellon University, Pittsburgh, PA, USA.
J Expo Sci Environ Epidemiol. 2022 Sep;32(5):712-719. doi: 10.1038/s41370-022-00411-2. Epub 2022 Jan 31.
The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks.
This paper describes a novel model for COVID-19 infection risks and policy evaluations.
The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies.
The application of the model is demonstrated for a typical office environment and for a real-world case.
The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.
COVID-19 大流行对经济有重大影响。企业重新开放的决策应考虑感染风险。
本文描述了一种用于 COVID-19 感染风险和政策评估的新型模型。
该模型结合了基于主体、微观暴露和概率建模方法的最佳原则。它考虑了工作场所的具体情况、口罩效率和员工的日常工作，但不需要模拟的特定代理间规则。同样，它也不需要了解微观疾病相关参数。相反，感染风险被汇总为感染概率，这取决于每次接触的持续时间和距离。使用严格的概率规则找到工作日结束时的感染概率。与以前的模型不同，这种方法只需要几个参考数据点进行校准，这些数据点可以通过实证研究更容易地收集。
该模型的应用在典型的办公环境和现实案例中进行了演示。
当疾病存在很大不确定性时，该模型可用于有效的风险评估和政策评估，因此特别适合 COVID-19 风险评估。
