Open AccessIndividual-based modeling of COVID-19 transmission in college communities
Author(s) -
Durward Cator,
Qimin Huang,
Anirban Mondal,
Martial L. Ndeffo-Mbah,
David Gurarie
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022646
Subject(s) - quarantine , psychological intervention , pandemic , outbreak , covid-19 , vaccination , environmental health , transmission (telecommunications) , robustness (evolution) , social distance , public health , disease , psychology , medicine , infectious disease (medical specialty) , computer science , virology , biology , telecommunications , biochemistry , nursing , pathology , psychiatry , gene
The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.