Individual-based simulation model for COVID-19 transmission in Daegu, Korea

Epidemiol Health. 2020:42:e2020042. doi: 10.4178/epih.e2020042. Epub 2020 Jun 15.

Abstract

Objectives: The aims of this study were to obtain insights into the current coronavirus disease 2019 (COVID-19) epidemic in the city of Daegu, which accounted for 6,482 of the 9,241 confirmed cases in Korea as of March 26, 2020, to predict the future spread, and to analyze the impact of school opening.

Methods: Using an individual-based model, we simulated the spread of COVID-19 in Daegu. An individual can be infected through close contact with infected people in a household, at work/school, and at religious and social gatherings. We created a synthetic population from census sample data. Then, 9,000 people were randomly selected from the entire population of Daegu and set as members of the Shincheonji Church. We did not take into account population movements to and from other regions in Korea.

Results: Using the individual-based model, the cumulative confirmed cases in Daegu through March 26, 2020, were reproduced, and it was confirmed that the hotspot, i.e., the Shincheonji Church had a different probability of infection than non-hotspot, i.e., the Daegu community. For 3 scenarios (I: school closing, II: school opening after April 6, III: school opening after April 6 and the mean period from symptom onset to hospitalization increasing to 4.3 days), we predicted future changes in the pattern of COVID-19 spread in Daegu.

Conclusions: Compared to scenario I, it was found that in scenario III, the cumulative number of patients would increase by 107 and the date of occurrence of the last patient would be delayed by 92 days.

Keywords: COVID-19; Infections; Korea; Mathematical model; Model prediction.

MeSH terms

  • Adolescent
  • Adult
  • COVID-19
  • Cities / epidemiology
  • Computer Simulation
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / prevention & control
  • Coronavirus Infections / transmission*
  • Epidemics*
  • Forecasting
  • Humans
  • Middle Aged
  • Models, Theoretical
  • Pandemics / prevention & control
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / prevention & control
  • Pneumonia, Viral / transmission*
  • Republic of Korea / epidemiology
  • Schools / organization & administration
  • Young Adult