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Mass Infection Analysis of COVID-19 Using the SEIRD Model in Daegu-Gyeongbuk of Korea from April to May, 2020
Tae Wuk Bae1, Kee Koo Kwon2, Kyu Hyung Kim2
1Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute, Daegu, Korea. twbae@etri.re.kr.
Insights
Mass infection events, like those seen in Korea, led to earlier COVID-19 peaks and slower recovery than predicted by standard epidemic models. Analyzing these time differences aids in predicting peaks and managing medical resources.
Area of Science:
- Epidemiology
- Infectious Disease Modeling
Background:
- The COVID-19 outbreak rapidly escalated in Korea due to mass (herd) infections in religious groups and nursing homes.
- Initial epidemic models underestimated the infection rate and recovery time compared to observed data.
Purpose of the Study:
- To evaluate the rapid spread of infection and high mortality rates in elderly and comorbid populations during mass infection events in Korea.
- To analyze discrepancies between actual infection trends and epidemic model predictions.
Main Methods:
- Utilized the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model.
- Compared actual infection data with model predictions for mass infection regions (Daegu, Gyeongbuk) versus normal infection regions.
Main Results:
- Demonstrated an earlier infection peak in mass infection regions (Daegu: -6.3 days, Gyeongbuk: -5.3 days) compared to model predictions.
- Observed a slower recovery trend in mass infection areas (Daegu: -1,486.6 persons, Gyeongbuk: -223.7 persons).
Conclusions:
- The time difference between infection and recovery is a critical factor for predicting epidemic peaks during mass or normal infection scenarios.
- This analysis provides a valuable time index for preparing and allocating medical resources effectively.
Background:
The novel coronavirus (coronavirus disease 2019 [COVID-19]) outbreak began in China in December last year, and confirmed cases began occurring in Korea in mid-February 2020. Since the end of February, the rate of infection has increased greatly due to mass (herd) infection within religious groups and nursing homes in the Daegu and Gyeongbuk regions. This mass infection has increased the number of infected people more rapidly than was initially expected; the epidemic model based on existing studies had predicted a much lower infection rate and faster recovery.
Methods:
The present study evaluated rapid infection spread by mass infection in Korea and the high mortality rate for the elderly and those with underlying diseases through the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model.
Results:
The present study demonstrated early infection peak occurrence (-6.3 days for Daegu and -5.3 days for Gyeongbuk) and slow recovery trend (= -1,486.6 persons for Daegu and -223.7 persons for Gyeongbuk) between the actual and the epidemic model for a mass infection region compared to a normal infection region.
Conclusion:
The analysis of the time difference between infection and recovery can help predict the epidemic peak due to mass (or normal) infection and can also be used as a time index to prepare medical resources.
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