Computational modeling of COVID-19 transmission and intervention strategies in South Korea
We present computational modeling of COVID-19 transmission and intervention strategies including vaccination and social-distancing. Furthermore, epidemiological distributions of the coronavirus disease 2019 (COVID-19), including the intervals from symptom onset to diagnosis, reporting, or death, are important for developing effective disease-control strategies. COVID-19 case data from a national database maintained by the Korea Disease Control and Prevention Agency (KDCA) and the Central Disease Control Headquarters were analysed.
A joint Bayesian subnational model with partial pooling was used and yielded probability distribution models of key epidemiological distributions in Korea. Furthermore, we estimate the extent of pre-symptomatic transmission in South Korea, by using individual-level COVID-19 case records from the KDCA and Central Disease Control Headquarters.
Eunha Shim is professor of Mathematics and chair of the Department of Mathematics at Soongsil University, South Korea. She obtained her BSc and MS at the University of British Columbia in 2002 and 2004, respectively. Later, she enrolled in the PhD program at Arizona State University, which she completed in 2007 under the supervision of Carlos Castillo-Chavez.
Dr Shim's research focuses on mathematical modeling of infectious diseases including influenza and economic analysis of disease intervention programs. She has extensive experience designing, analysing, and simulating mathematical models of infectious diseases to inform public health policies and to project the potential impact of intervention strategies.