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

Bong Gu Kang,Hee-Mun Park,Mi Jang,Kyung-Min Seo

doi:10.3390/ijerph182111264

Abstract

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.

Highlights

During the initial outbreak of coronavirus disease (COVID-19), the major propagation factors were population movement and cluster infections in groups [1,2,3]
This study presents a practical modeling and simulation (M&S) approach to analyze the trends of the COVID-19 epidemic
In the model identification step, to solve this problem, we identified the constructed infection simulation model using a data set acquired from the real-world and simulation optimizer [35], which calibrates the parameters in the simulation model using the optimization algorithms [36,37,38]

Summary

Introduction

During the initial outbreak of coronavirus disease (COVID-19), the major propagation factors were population movement and cluster infections in groups [1,2,3]. In South Korea, for example, several situations exploded due to carriers in groups such as churches and hospitals [4]. To analyze how social distancing is effective in COVID-19 prevention, we utilize modeling and simulation (M&S) methods in this study. Simulations containing population movement are intrinsically complicated, well-categorized models are understandable, analyzable, and certifiable [7,8,9,10]. They give helpful insights to analyze and predict COVID-19 situations [11,12,13]. Based on real-world data regarding confirmed cases in South Korea, the modeling requirements for the COVID-19 simulation are twofold:

Methods

Discussion

Conclusion