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Clinical Time Delay Distributions of COVID-19 in 2020-2022 in the Republic of Korea: Inferences from a Nationwide Database Analysisopen access

Authors
Shim, EunhaChoi, WongyeongSong, Youngji
Issue Date
Jun-2022
Publisher
MDPI
Keywords
COVID-19; epidemiological distribution; Delta variant; Republic of Korea; serial interval; SARS-CoV-2
Citation
JOURNAL OF CLINICAL MEDICINE, v.11, no.12
Journal Title
JOURNAL OF CLINICAL MEDICINE
Volume
11
Number
12
URI
http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42583
DOI
10.3390/jcm11123269
ISSN
2077-0383
Abstract
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 19 January 2020 to 10 January 2022) from a national database maintained by the Korea Disease Control and Prevention Agency and the Central Disease Control Headquarters were analyzed. A joint Bayesian subnational model with partial pooling was used and yielded probability distribution models of key epidemiological distributions in Korea. Serial intervals from before and during the Delta variant's predominance were estimated. Although the mean symptom-onset-to-report interval was 3.2 days at the national level, it varied across different regions (2.9-4.0 days). Gamma distribution showed the best fit for the onset-to-death interval (with heterogeneity in age, sex, and comorbidities) and the reporting-to-death interval. Log-normal distribution was optimal for ascertaining the onset-to-diagnosis and onset-to-report intervals. Serial interval (days) was shorter before the Delta variant-induced outbreaks than during the Delta variant's predominance (4.4 vs. 5.2 days), indicating the higher transmission potential of the Delta variant. The identified heterogeneity in region-, age-, sex-, and period-based distributions of the transmission dynamics of COVID-19 will facilitate the development of effective interventions and disease-control strategies.
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College of Natural Sciences (Department of Mathematics)
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