NDLI: Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

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Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

Content Provider	MDPI
Author	Kang, Bong Gu Park, Hee-Mun Jang, Mi Seo, Kyung-Min
Copyright Year	2021
Description	This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.
Starting Page	11264
e-ISSN	16604601
DOI	10.3390/ijerph182111264
Journal	International Journal of Environmental Research and Public Health
Issue Number	21
Volume Number	18
Language	English
Publisher	MDPI
Publisher Date	2021-10-27
Access Restriction	Open
Subject Keyword	International Journal of Environmental Research and Public Health Simulation Sird Model Discrete-event Model Data-based Learning Covid-19 Epidemic
Content Type	Text
Resource Type	Article

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