Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature

South Korea implemented interventions to curb the spread of the novel coronavirus disease 2019 (COVID-19) pandemic with discovery of the first case in early 2020. Mathematical modeling designed to reflect the dynamics of disease transmission has been shown to be an important tool for responding to COVID-19. This study aimed to review publications on the structure, method, and role of mathematical models focusing on COVID-19 transmission dynamics in Korea. In total, 42 papers published between August 7, 2020 and August 21, 2022 were studied and reviewed. This study highlights the construction and utilization of mathematical models to help craft strategies for predicting the course of an epidemic and evaluating the effectiveness of control strategies. Despite the limitations caused by a lack of available epidemiological and surveillance data, modeling studies could contribute to providing scientific evidence for policymaking by simulating various scenarios.

[1]  J. Ballreich,et al.  Modeling the potential economic benefits of an oral SARS-CoV-2 vaccine during an outbreak of COVID-19 , 2022, BMC Public Health.

[2]  O. Taylan,et al.  Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches , 2022, Financial innovation.

[3]  Renier G. Mendoza,et al.  Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study , 2022, Journal of Korean medical science.

[4]  Jung Eun Kim,et al.  Booster Vaccination Strategies for “Living With COVID-19” , 2022, Frontiers in Public Health.

[5]  Y. Jo,et al.  Optimal Social Distancing Policy for COVID-19 Control in Korea: A Model-Based Analysis , 2022, Journal of Korean medical science.

[6]  Fang Wang,et al.  Mathematical modeling of mutated COVID-19 transmission with quarantine, isolation and vaccination. , 2022, Mathematical biosciences and engineering : MBE.

[7]  Han Liu,et al.  Sociodemographic and Policy Factors Associated with the Transmission of COVID-19: Analyzing Longitudinal Contact Tracing Data from a Northern Chinese City , 2022, Journal of Urban Health.

[8]  Yongdai Kim,et al.  Age-Varying Susceptibility to the Delta Variant (B.1.617.2) of SARS-CoV-2 , 2022, JAMA network open.

[9]  Hyojung Lee,et al.  Forecasting COVID-19 cases by assessing control-intervention effects in Republic of Korea: A statistical modeling approach , 2022, Alexandria Engineering Journal.

[10]  Chi Seng Pun,et al.  Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations , 2021, European Journal of Operational Research.

[11]  Sangwoo Tak,et al.  Reconstructing a COVID-19 outbreak within a religious group using social network analysis simulation in Korea , 2021, Epidemiology and health.

[12]  E. Jung,et al.  Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study , 2021, Epidemiology and health.

[13]  Woo-Sik Son,et al.  Rapid transmission of coronavirus disease 2019 within a religious sect in South Korea: A mathematical modeling study , 2021, Epidemics.

[14]  Arman Canatay,et al.  Critical country-level determinants of death rate during Covid-19 pandemic , 2021, International Journal of Disaster Risk Reduction.

[15]  E. Shim.  Projecting the Impact of SARS-CoV-2 Variants and the Vaccination Program on the Fourth Wave of the COVID-19 Pandemic in South Korea , 2021, International journal of environmental research and public health.

[16]  E. Jung,et al.  COVID-19 Vaccine Priority Strategy Using a Heterogenous Transmission Model Based on Maximum Likelihood Estimation in the Republic of Korea , 2021, International journal of environmental research and public health.

[17]  Sangwoo Tak,et al.  Dynamics of the COVID-19 epidemic in the post-vaccination period in Korea: a rapid assessment , 2021, Epidemiology and health.

[18]  T. Park,et al.  Estimation of Undetected Asymptomatic COVID-19 Cases in South Korea Using a Probabilistic Model , 2021, International journal of environmental research and public health.

[19]  M. You,et al.  A social network analysis of the spread of COVID-19 in South Korea and policy implications , 2021, Scientific Reports.

[20]  Chang Hyeong Lee,et al.  Vaccination Prioritization Strategies for COVID-19 in Korea: A Mathematical Modeling Approach , 2021, International journal of environmental research and public health.

[21]  H. Kwon,et al.  The effect of control measures on COVID-19 transmission in South Korea , 2021, PloS one.

[22]  S. Mahmoudi,et al.  A dynamic model for the COVID‐19 with direct and indirect transmission pathways , 2021, Mathematical methods in the applied sciences.

[23]  E. Shim.  Optimal Allocation of the Limited COVID-19 Vaccine Supply in South Korea , 2021, Journal of clinical medicine.

[24]  Hyojung Lee,et al.  Subcritical Transmission in the Early Stage of COVID-19 in Korea , 2021, International journal of environmental research and public health.

[25]  Hee-Young Shin.  A multi-stage SEIR(D) model of the COVID-19 epidemic in Korea , 2021, Annals of medicine.

[26]  Chaeyoung Lee,et al.  Controlling COVID-19 Outbreaks with Financial Incentives , 2021, International journal of environmental research and public health.

[27]  Hyojung Lee,et al.  Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach , 2020, JMIR public health and surveillance.

[28]  J. Heffernan,et al.  Cost and social distancing dynamics in a mathematical model of COVID-19 with application to Ontario, Canada , 2020, Royal Society Open Science.

[29]  Sunmi Lee,et al.  Assessing the Effectiveness of Isolation and Contact-Tracing Interventions for Early Transmission Dynamics of COVID-19 in South Korea , 2021, IEEE Access.

[30]  J. Kim,et al.  Intervention analysis for spread of COVID-19 in South Korea using SIR model , 2021 .

[31]  E. Shim,et al.  Optimal strategies for social distancing and testing to control COVID-19 , 2020, Journal of Theoretical Biology.

[32]  A. Heidarzadeh,et al.  Seroprevalence of SARS-CoV-2 in Guilan Province, Iran, April 2020 , 2020, Emerging infectious diseases.

[33]  E. Jung,et al.  Keeping Low Reproductive Number Despite the Rebound Population Mobility in Korea, a Country Never under Lockdown during the COVID-19 Pandemic , 2020, International journal of environmental research and public health.

[34]  Jeehyun Lee,et al.  A mathematical model of COVID-19 transmission in a tertiary hospital and assessment of the effects of different intervention strategies , 2020, PloS one.

[35]  M. Z. Ferdous,et al.  Forecasting the spread of COVID-19 pandemic in Bangladesh using ARIMA model , 2020, medRxiv.

[36]  Chang Hyeong Lee,et al.  Assessment of Social Distancing for Controlling COVID-19 in Korea: An Age-Structured Modeling Approach , 2020, International journal of environmental research and public health.

[37]  Sunmi Lee,et al.  Mathematical Model of COVID-19 Transmission Dynamics in South Korea: The Impacts of Travel Restrictions, Social Distancing, and Early Detection , 2020, Processes.

[38]  E. Jung,et al.  The impact of social distancing and public behavior changes on COVID-19 transmission dynamics in the Republic of Korea , 2020, PloS one.

[39]  M. Ki,et al.  Analyzing the Effect of Social Distancing for Novel Coronavirus Disease 2019(COVID-19) in South Korea. , 2020, Epidemiology and health.

[40]  Se Young Jung,et al.  Real-World Implications of a Rapidly Responsive COVID-19 Spread Model with Time-Dependent Parameters via Deep Learning: Model Development and Validation , 2020, Journal of medical Internet research.

[41]  Kee-Koo Kwon,et al.  Mass Infection Analysis of COVID-19 Using the SEIRD Model in Daegu-Gyeongbuk of Korea from April to May, 2020 , 2020, Journal of Korean medical science.

[42]  Sung-il Cho,et al.  Estimating the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 Control in Korea , 2020, Journal of Korean medical science.

[43]  C. Steves,et al.  Estimates of the rate of infection and asymptomatic COVID-19 disease in a population sample from SE England , 2020, Journal of Infection.

[44]  M. Han,et al.  Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey , 2020, JMIR medical informatics.

[45]  Hojeong Park,et al.  A Study on Herd Immunity of COVID-19 in South Korea: Using a Stochastic Economic-Epidemiological Model , 2020, Environmental and Resource Economics.

[46]  Woo-Sik Son,et al.  Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation , 2020, Epidemiology and health.

[47]  M. Seo,et al.  Estimating a breakpoint in the pattern of spread of COVID-19 in South Korea , 2020, International Journal of Infectious Diseases.

[48]  Woo-Sik Son.  Individual-based simulation model for COVID-19 transmission in Daegu, Korea , 2020, Epidemiology and health.

[49]  Zhen Jin,et al.  Phase-adjusted estimation of the COVID-19 outbreak in South Korea under multi-source data and adjustment measures: a modelling study. , 2020, Mathematical biosciences and engineering : MBE.

[50]  Hsien-Ho Lin,et al.  Contact Tracing Assessment of COVID-19 Transmission Dynamics in Taiwan and Risk at Different Exposure Periods Before and After Symptom Onset , 2020, JAMA internal medicine.

[51]  S. Ryu,et al.  Estimation of the Excess COVID-19 Cases in Seoul, South Korea by the Students Arriving from China , 2020, International journal of environmental research and public health.

[52]  E. Jung,et al.  Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea , 2020, Epidemiology and health.

[53]  E. Jung,et al.  Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes. , 2020, Epidemiology and health.

[54]  M. Ki,et al.  Estimating the reproductive number and the outbreak size of Novel Coronavirus disease (COVID-19) using mathematical model in Republic of Korea. , 2020, Epidemiology and health.

[55]  Eunok Jung,et al.  School Opening Delay Effect on Transmission Dynamics of Coronavirus Disease 2019 in Korea: Based on Mathematical Modeling and Simulation Study , 2020, Journal of Korean medical science.

[56]  M. Ajelli,et al.  Reactive school closure weakens the network of social interactions and reduces the spread of influenza , 2019, Proceedings of the National Academy of Sciences.

[57]  Mark Jit,et al.  Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong , 2017, Scientific Reports.

[58]  R. White,et al.  Age- and Sex-Specific Social Contact Patterns and Incidence of Mycobacterium tuberculosis Infection , 2015, American journal of epidemiology.

[59]  Yasushi Ohkusa,et al.  Social contacts, vaccination decisions and influenza in Japan , 2015, Journal of Epidemiology & Community Health.

[60]  N. Hens,et al.  The French Connection: The First Large Population-Based Contact Survey in France Relevant for the Spread of Infectious Diseases , 2015, PloS one.

[61]  N. Hens,et al.  A Household-Based Study of Contact Networks Relevant for the Spread of Infectious Diseases in the Highlands of Peru , 2015, PloS one.

[62]  D. Cummings,et al.  Social mixing patterns in rural and urban areas of southern China , 2014, Proceedings of the Royal Society B: Biological Sciences.

[63]  P. Horby,et al.  Social Contact Patterns in Vietnam and Implications for the Control of Infectious Diseases , 2011, PloS one.

[64]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[65]  Constance A Nyamukapa,et al.  HIV Decline Associated with Behavior Change in Eastern Zimbabwe , 2006, Science.