Hritonenko Natali, Yatsenko Olga, Yatsenko Yuri
Department of Mathematics Prairie View A&M University Prairie View Texas USA.
Department of Health Promotion & Behavioral Sciences University of Texas Health Science Center at Houston Houston Texas USA.
J Public Econ Theory. 2021 Nov 23. doi: 10.1111/jpet.12554.
The paper focuses on modeling of public health measures to control the COVID-19 pandemic. The authors suggest a flexible integral model with distributed lags, which realistically describes COVID-19 infectiousness period from clinical data. It contains susceptible-infectious-recovered (SIR), susceptible-exposed-infectious-recovered (SEIR), and other epidemic models as special cases. The model is used for assessing how government decisions to lockdown and reopen the economy affect epidemic spread. The authors demonstrate essential differences in transition and asymptotic dynamics of the integral model and the SIR model after lockdown. The provided simulation on real data accurately describes several waves of the COVID-19 epidemic in the United States and is in good correspondence with government actions to curb the epidemic.
本文聚焦于控制新冠疫情大流行的公共卫生措施建模。作者提出了一种具有分布滞后的灵活积分模型，该模型从临床数据出发，切实地描述了新冠病毒的传染期。它包含易感-感染-康复（SIR）模型、易感-暴露-感染-康复（SEIR）模型以及其他流行病模型作为特殊情况。该模型用于评估政府封锁和重新开放经济的决策如何影响疫情传播。作者展示了封锁后积分模型和SIR模型在过渡和渐近动态方面的本质差异。基于实际数据进行的模拟准确地描述了美国新冠疫情的几波情况，并且与政府遏制疫情的行动高度吻合。
