Study develops a novel model that estimates undetected COVID-19 emergence PLOSONE BeALonghorn covid COVID19 SARSCoV2 emergence news novel methods model epidemic pandemic health publichealthnews
By Dr. Chinta SidharthanApr 10 2023Reviewed by Lily Ramsey, LLM In a recent study published in the PLOS ONE Journal, researchers developed a model framework to estimate the probability of an undetected epidemic using the number of suspected and confirmed cases and the epidemiological characteristics of the pathogen.
With limited data on the number of cases and a lack of clarity on the characteristics of the virus, the local governments faced the challenge of implementing safety measures that could be socially and economically detrimental. About the study In the present study, the researchers accounted for factors such as transmission risk, asymptomatic infections, potential events of super spreading, and epidemiological characteristics of the disease while modeling the stochastic emergence severe acute respiratory syndrome coronavirus 2 .
The susceptible-exposed-infected-recovered model used the number of reported cases to estimate the probability of an outbreak in a county or region expanding into an epidemic based on the assumption that no public health interventions or disease mitigation measures were implemented. In the retrospective analysis that included a reproduction number specific for each county, the estimates suggested counties with one confirmed COVID-19 by March 16, 2020, were at a mean epidemic risk of 71%.
Considerable undocumented transmission of COVID-19 was detected by phylodynamic and epidemiological models until the social-distancing, lockdowns, and other non-pharmaceutical measures were implemented to reduce the transmission of SARS-CoV-2.
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