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Epidemiology and Health 2021;e2021068.
DOI: https://doi.org/10.4178/epih.e2021068    [Accepted] Published online Sep 16, 2021.
Reconstructing a COVID-19 outbreak within a religious group using social network analysis simulation
Namje Kim1,2  , Su Jin Kang3  , Sangwoo Tak3 
1Department of Economics, Seoul National University, Seoul, Korea
2Institute of Research in Finance and Economics, Seoul National University, Seoul, Korea
3Seoul National University, Seoul, Korea
Correspondence  Sangwoo Tak ,Tel: 02-880-2802, Fax: 2019-04-10 위촉, Email: taksw@snu.ac.kr
Received: Jun 3, 2021  Accepted after revision: Sep 16, 2021
Objective: We reconstructed the outbreak of the COVID-19 to understand how a large cluster at a church setting progressed before being detected and estimate the potential effectiveness of complying with mask-wearing guidelines recommended by the government.
A mathematical model with social network analysis approach (SNA) was used to simulate a COVID-19 outbreak. A discrete-time, stochastic simulation model was used to construct COVID-19 spread within the SJ Church. A counterfactual experiment using a calibrated baseline model was conducted to examine the potential benefit of complying with mask-wearing policy.
Simulations with a mask-wearing percentage of 60.5% (range 55–65%) at the time of the outbreak yielded results most consistent with the confirmed case data. If the church had followed government mask-wearing guidelines, the outbreak might have been one-tenth the size. The counterfactual experiment with 95% mask-wearing estimated an average of 45.6 (95% CI: 43.4-47.9) cases with a standard deviation of 20.1. Discussion: SNA is an effective tool for monitoring and controlling outbreaks of COVID-19 and other infectious diseases. Although our results are based on simulations and thus subject to limitations, the precautionary implication of social distancing and mask-wearing is still relevant. Because person-to-person contact/interaction is unavoidable in social and economic life, it may be wise to consider developing precise measures and guidelines for particular organizations or places that are susceptible to cluster outbreaks.
Keywords: Social network analysis; COVID-19; Non-pharmaceutical intervention
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