ArticleComputer methods in applied mechanics and engineering2020
Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.
Article in Computer methods in applied mechanics and engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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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
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned.Archives of public health = Archives belges de sante publique · 2026Pooled it
- Differentiating Contact with Symptomatic and Asymptomatic Infectious Individuals in a SEIR Epidemic Model.Bulletin of mathematical biology · 2025Article
- Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.Small methods · 2025Article
- Seroprevalence of COVID-19 in Oran: Cross-Sectional Study.Microbiology spectrum · 2023Article
- Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.Scientific reports · 2023Article
- The Role of Mobility in the Dynamics of the COVID-19 Epidemic in Andalusia.Bulletin of mathematical biology · 2023Article
- A novel strategy to avoid sensitivity loss in pooled testing for SARS-CoV-2 surveillance: validation using nasopharyngeal swab and saliva samples.Frontiers in public health · 2023Article
- Ultra-high field cardiac MRI in large animals and humans for translational cardiovascular research.Frontiers in cardiovascular medicine · 2023Article
- Article
- Virus spread on a scale-free network reproduces the Gompertz growth observed in isolated COVID-19 outbreaks.Advances in biological regulation · 2022Article
- Backcasting COVID-19: a physics-informed estimate for early case incidence.Royal Society open science · 2022Article
- What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?BMC infectious diseases · 2022Article
- A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Application to COVID-19.Viruses · 2022Article
- Assessing the spatial variability of raising public risk awareness for the intervention performance of COVID-19 voluntary screening: A spatial simulation approach.Applied geography (Sevenoaks, England) · 2022Article
- Seroprevalence of IgM and IgG anti-SARS-COV-2 and associated factors among agricultural workers in Colombia.New microbes and new infections · 2022Article
- COVID-19 mortality in Italy varies by patient age, sex and pandemic wave.Scientific reports · 2022Article
- Article
- Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.Medical review (2021) · 2022Review
- Core policies disparity response to COVID-19 among BRICS countries.International journal for equity in health · 2022Article
- Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities.Computer methods in applied mechanics and engineering · 2021Article
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7 authors.
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Abstract
Understanding the outbreak dynamics of the COVID-19 pandemic has important implications for successful containment and mitigation strategies. Recent studies suggest that the population prevalence of SARS-CoV-2 antibodies, a proxy for the number of asymptomatic cases, could be an order of magnitude larger than expected from the number of reported symptomatic cases. Knowing the precise prevalence and contagiousness of asymptomatic transmission is critical to estimate the overall dimension and pandemic potential of COVID-19. However, at this stage, the effect of the asymptomatic population, its size, and its outbreak dynamics remain largely unknown. Here we use reported symptomatic case data in conjunction with antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19. Our model computes, in real time, the time-varying contact rate of the outbreak, and projects the temporal evolution and credible intervals of the effective reproduction number and the symptomatic, asymptomatic, and recovered populations. Our study quantifies the sensitivity of the outbreak dynamics of COVID-19 to three parameters: the effective reproduction number, the ratio between the symptomatic and asymptomatic populations, and the infectious periods of both groups. For nine distinct locations, our model estimates the fraction of the population that has been infected and recovered by Jun 15, 2020 to 24.15% (95% CI: 20.48%-28.14%) for Heinsberg (NRW, Germany), 2.40% (95% CI: 2.09%-2.76%) for Ada County (ID, USA), 46.19% (95% CI: 45.81%-46.60%) for New York City (NY, USA), 11.26% (95% CI: 7.21%-16.03%) for Santa Clara County (CA, USA), 3.09% (95% CI: 2.27%-4.03%) for Denmark, 12.35% (95% CI: 10.03%-15.18%) for Geneva Canton (Switzerland), 5.24% (95% CI: 4.84%-5.70%) for the Netherlands, 1.53% (95% CI: 0.76%-2.62%) for Rio Grande do Sul (Brazil), and 5.32% (95% CI: 4.77%-5.93%) for Belgium. Our method traces the initial outbreak date in Santa Clara County back to January 20, 2020 (95% CI: December 29, 2019-February 13, 2020). Our results could significantly change our understanding and management of the COVID-19 pandemic: A large asymptomatic population will make isolation, containment, and tracing of individual cases challenging. Instead, managing community transmission through increasing population awareness, promoting physical distancing, and encouraging behavioral changes could become more relevant.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.