Repository logo
  • Log In
    Log in via Symplectic to deposit your publication(s).
Repository logo
  • About
  • Communities & Collections
  • Advanced Search
  • Statistics
  • Log In
    Log in via Symplectic to deposit your publication(s).
  1. Home
  2. Faculty of Medicine
  3. Faculty of Medicine
  4. Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study
 
  • Details
Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study
File(s)
1-s2.0-S1755436523000282-main.pdf (1.24 MB)
Published version
Author(s)
Bhatia, Sangeeta
Wardle, Jack
Nash, Rebecca
Nouvellet, Pierre
Cori, Anne
Type
Journal Article
Abstract
The evolution of SARS-CoV-2 has demonstrated that emerging variants can set back the global COVID-19 response. The ability to rapidly assess the threat of
new variants is critical for timely optimisation of control strategies.
We present a novel method to estimate the effective transmission advantage of a new variant compared to a reference variant combining information across multiple locations and over time. Through an extensive simulation study designed to mimic real-time epidemic contexts, we show that our method performs well across a range of scenarios and provide guidance on its optimal use
and interpretation of results. We also provide an open-source software implementation of our method. The computational speed of our tool enables users to
rapidly explore spatial and temporal variations in the estimated transmission advantage.
We estimate that the SARS-CoV-2 Alpha variant is 1.46 (95% Credible Interval 1.44-1.47) and 1.29, (95% CrI 1.29-1.30) times more transmissible than the wild type, using data from England and France respectively. We further
estimate that Delta is 1.77 (95% CrI: 1.69-1.85) times more transmissible than Alpha (England data).
Our approach can be used as an important first step towards quantifying the threat of emerging or co-circulating variants of infectious pathogens in real-time.
Date Issued
2023-09
Date Acceptance
2023-05-29
Citation
Epidemics: the journal of infectious disease dynamics, 2023, 44, pp.1-8
URI
http://hdl.handle.net/10044/1/104966
URL
https://www.sciencedirect.com/science/article/pii/S1755436523000282
DOI
https://www.dx.doi.org/10.1016/j.epidem.2023.100692
ISSN
1755-4365
Publisher
Elsevier
Start Page
1
End Page
8
Journal / Book Title
Epidemics: the journal of infectious disease dynamics
Volume
44
Copyright Statement
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
http://creativecommons.org/licenses/by/4.0/
Identifier
https://www.sciencedirect.com/science/article/pii/S1755436523000282
Publication Status
Published
Article Number
100692
Date Publish Online
2023-06-21
About
Spiral Depositing with Spiral Publishing with Spiral Symplectic
Contact us
Open access team Report an issue
Other Services
Scholarly Communications Library Services
logo

Imperial College London

South Kensington Campus

London SW7 2AZ, UK

tel: +44 (0)20 7589 5111

Accessibility Modern slavery statement Cookie Policy

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback