ArticleScientific reports2025
Estimation of undetected asymptomatic infections of COVID-19: a mathematical modeling approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Asymptomatic COVID-19 among university students during the low-risk transmission period.BMC infectious diseases · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
The accurate quantification of asymptomatic infections is crucial for understanding and mitigating the spread of COVID-19. However, estimating the true prevalence of asymptomatic cases remains a significant challenge. This study introduces a novel mathematical modeling approach to estimate key epidemiological parameters associated with asymptomatic infections, leveraging comprehensive COVID-19 data from the Republic of Korea. We develop a refined compartmental model that explicitly incorporates asymptomatic individuals and employs a trajectory matching method, integrating least-squares fitting with gradient matching, to align the model with confirmed cases and deaths. The model successfully reproduces the initial outbreak and subsequent epidemic waves, demonstrating strong agreement with observed data and validating the estimated parameters, which align with prior findings. We observe a progressive increase in both infectivity and the proportion of asymptomatic infections from the initial strain to the Delta and Omicron variants. Notably, across all phases, the proportion of asymptomatic cases is higher among vaccinated individuals compared to unvaccinated individuals. This study provides valuable insights into the hidden dynamics of the COVID-19 pandemic and offers a robust methodology that can be broadly applied to estimate critical epidemiological parameters for other infectious diseases, enhancing our capacity to manage future outbreaks.
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