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Estimation of Undetected Asymptomatic COVID-19 Cases in South Korea Using a Probabilistic Model.
Lee, Chanhee; Apio, Catherine; Park, Taesung.
  • Lee C; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
  • Apio C; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
  • Park T; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Int J Environ Res Public Health ; 18(9)2021 05 06.
Article in English | MEDLINE | ID: covidwho-1223999
ABSTRACT
Increasing evidence shows that many infections of COVID-19 are asymptomatic, becoming a global challenge, since asymptomatic infections have the same infectivity as symptomatic infections. We developed a probabilistic model for estimating the proportion of undetected asymptomatic COVID-19 patients in the country. We considered two scenarios one is conservative and the other is nonconservative. By combining the above two scenarios, we gave an interval estimation of 0.0001-0.0027 and in terms of the population, 5200-139,900 is the number of undetected asymptomatic cases in South Korea as of 2 February 2021. In addition, we provide estimates for total cases of COVID-19 in South Korea. Combination of undetected asymptomatic cases and undetected symptomatic cases to the number of confirmed cases (78,844 cases on 2 February 2021) shows that 0.17-0.42% (89,244-218,744) of the population have COVID-19. In conclusion, to control and understand the true ongoing reality of the pandemic, it is of outermost importance to focus on the ratio of undetected asymptomatic cases in the total population.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Asia Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Asia Language: English Year: 2021 Document Type: Article