JOURNAL ARTICLE

Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes.

  • Published In: Journal of the Royal Statistical Society: Series A (Statistics in Society), 2023, v. 186, n. 4. P. 601 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Bhatt, Samir; Ferguson, Neil; Flaxman, Seth; Gandy, Axel; Mishra, Swapnil; Scott, James A 3 of 3

Abstract

This article presents a general Bayesian approach to modeling epidemics like COVID-19, focusing on estimating the effects of non-pharmaceutical interventions (NPIs) on transmission in 11 European countries. The model uses a multilevel regression framework to parameterize the time-varying reproduction number (R_t) based on covariates such as governmental interventions and mobility changes, allowing data sharing across regions through partial pooling. This approach enabled timely and validated estimates of lockdown impacts and was applied by regions including New York State, Tennessee, and Scotland for epidemic assessment and policy guidance. The framework also generates latent infection estimates and related observations like deaths, hospitalizations, and seroprevalence surveys.

Additional Information

  • Source:Journal of the Royal Statistical Society: Series A (Statistics in Society). 2023/10, Vol. 186, Issue 4, p601
  • Document Type:Article
  • Subject Area:Sociology
  • Publication Date:2023
  • ISSN:0964-1998
  • DOI:10.1093/jrsssa/qnad030
  • Accession Number:174783675
  • Copyright Statement:Copyright of Journal of the Royal Statistical Society: Series A (Statistics in Society) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.