Using Agent Based Models on small world networks to understand disease transmission

Project Title: Using Agent Based Models on small world networks to understand disease transmission
Program:
Northeast (308)

Project Leader
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Project Leader:
Jane Molofsky

Email: jane.molofsky@uvm.edu
Mobile Phone: 802-363-7664
Work Phone: {Empty}

Project Information
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Project Description:
We would like to model the spread of the SARS COVID-19 virus on a college campus by using an Agent Based Model in a small world network. We began this project during the NET-COVID online meeting hosted by the University of Vermont Complex Systems Center and the University Maryland COMBINE program. The results of this work is currently in review at PLOSONE and archived at http://arxiv.org/abs/2005.09751

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We investigate how  three social distance remediation measures work alone (1. Top down social distancing imposed on the entire population,2) self-isolation following contact with an infected individual and 3) viral shedding remediated through mask wearing) and interact with each other to control the epidemic of COVID-19. Our results show that there is a threshold effect. When social distancing measures are at or above 75%, the viral infection rate is very low.  If social distancing measures are between 60-75%, self-isolation of exposed individuals following contact with an infected person can also keep viral outbreaks small. Finally, the adoption of mask wearing by large numbers of the populations can contain the outbreak if social distancing is kept at levels of around 60%.