Imperial College London
Alternate

Professor Neil Ferguson

Faculty of Medicine, School of Public Health

Director of the School of Public Health
 
 
 
//

Contact

 

+44 (0)20 7594 3296neil.ferguson Website

 
 
//

Location

 

508School of Public HealthWhite City Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Unwin:2021:10.1101/2021.02.24.21252339,
author = {Unwin, HJT and Cori, A and Imai, N and Gaythorpe, KAM and Bhatia, S and Cattarino, L and Donnelly, CA and Ferguson, NM and Baguelin, M},
doi = {10.1101/2021.02.24.21252339},
title = {Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak},
url = {http://dx.doi.org/10.1101/2021.02.24.21252339},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:p>Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 – 16.0%) but depending on assumptions 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 – 87.0% or 1.7 – 80.9%).</jats:p>
AU  - Unwin,HJT
AU  - Cori,A
AU  - Imai,N
AU  - Gaythorpe,KAM
AU  - Bhatia,S
AU  - Cattarino,L
AU  - Donnelly,CA
AU  - Ferguson,NM
AU  - Baguelin,M
DO  - 10.1101/2021.02.24.21252339
PY  - 2021///
TI  - Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak
UR  - http://dx.doi.org/10.1101/2021.02.24.21252339
ER  -
Download