ABSTRACT

In the early development of COVID-19, large-scale preventive measures, such as border control and air travel restrictions, were implemented to slow international and domestic transmissions. When these measures were in full effect, new cases of infection would be primarily induced by community spread, such as the human interaction within and between neighboring cities and towns, which is generally known as the meso-scale. Existing studies of COVID-19 using mathematical models are unable to accommodate the need for meso-scale modeling, because of the unavailability of COVID-19 data at this scale and the different timings of local intervention policies. In this respect, we propose a meso-scale mathematical model of COVID-19, named the meso-scale Susceptible, Exposed, Infectious, Recovered (MSEIR) model, using town-level infection data in the state of Connecticut. We consider the spatial interaction in terms of the inter-town travel in the model. Based on the developed model, we evaluated how different strengths of social distancing policy enforcement may impact epi curves based on two evaluative metrics: compliance and containment. The developed model and the simulation results help to establish the foundation for community-level assessment and better preparedness for COVID-19.

摘要

在COVID-19的早期开发中，采取了大规模的预防措施，例如边境管制和航空旅行限制，以减缓国际和国内的传播。当这些措施完全生效时，新的感染病例将主要由社区传播引起，例如邻近城镇之间及其之间的人与人之间的互动，通常称为中尺度。现有的使用数学模型进行的COVID-19研究无法满足中尺度建模的需要，因为在该尺度上没有COVID-19数据，而且当地干预政策的时机不同。在这方面，我们提出了COVID-19的中尺度数学模型，称为中尺度易感，暴露，传染性，恢复（MSEIR）模型，使用康涅狄格州的城镇感染数据。我们在模型中考虑了城际旅行的空间相互作用。基于已开发的模型，我们基于两个评估指标：合规性和遏制力，评估了社会疏远政策执行的不同优势如何影响Epi曲线。所开发的模型和仿真结果有助于为社区级别的评估和为COVID-19做好更好的准备奠定基础。