Simulating and Epidemic Prediction of COVID-19 Transmission in Universities Considering Different Interventions

Abstract

Pandemic diseases are fought through efficient intervention procedures including non-pharmaceutical interventions such as social distancing, and closure of events, schools and workplaces. These procedures are ruled by different decisions-makers in local communities, and national authorities. Different procedures are conducted based on the community affected by the pandemic disease to reduce the severity in various social life. During the Coronavirus (COVID-19) pandemic most of universities worldwide closed their campuses and continue the study through online learning. To open back the campuses, universities have to analyze and investigate such decision impact on the outbreaks. This paper develops a simulation model of COVID-19 investigating realistic intervention procedures that can be conducted to control and mitigate outbreaks. The model is constructed to simulate Effat University environment. Our model predicts average COVID-19 attack rates for various intervention scenarios, such as partial closure and social distancing, cleaning and disinfection, COVID-19 symptoms monitoring system are combined with isolate and treat confirmed COVID-19 cases. The findings indicate that combining all the proposed interventions can be substantially more effective than isolate and treat confirmed COVID-19 cases alone from epidemiological standpoint.