Compliance and containment in social distancing: mathematical modeling of COVID-19 across townships

Xiang Chen, Aiyin Zhang, Hui Wang, Adam Gallaher, Xiaolin Zhu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)446-465
Number of pages20
JournalInternational Journal of Geographical Information Science
Volume35
Issue number3
DOIs
Publication statusPublished - Jan 2021

Keywords

  • COVID-19
  • epidemic model
  • mobility
  • social distancing
  • spatial interaction

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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