Your browser doesn't support javascript.
loading
Visualizing the invisible: The effect of asymptomatic transmission on the outbreak dynamics of COVID-19.
Peirlinck, Mathias; Linka, Kevin; Costabal, Francisco Sahli; Bhattacharya, Jay; Bendavid, Eran; Ioannidis, John P A; Kuhl, Ellen.
Afiliación
  • Peirlinck M; Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, United States.
  • Linka K; Department of Mechanical Engineering, Stanford University School of Engineering, Stanford, California, United States.
  • Costabal FS; Department of Mechanical and Metallurgical Engineering and Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile.
  • Bhattacharya J; Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
  • Bendavid E; Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
  • Ioannidis JPA; Department of Medicine, Stanford University School of Medicine, Stanford, California, United States.
  • Kuhl E; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, United States.
medRxiv ; 2020 Aug 29.
Article en En | MEDLINE | ID: mdl-32869035
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.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: MedRxiv Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos