Imperial College London
Alternate

DrPierreNouvellet

Faculty of Medicine, School of Public Health

Visiting Reader
 
 
 
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Contact

 

p.nouvellet

 
 
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Location

 

UG 11Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nash:2022:10.1371/journal.pdig.0000052,
author = {Nash, RK and Nouvellet, P and Cori, A},
doi = {10.1371/journal.pdig.0000052},
journal = {PLOS Digital Health},
pages = {e0000052--e0000052},
title = {Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges},
url = {http://dx.doi.org/10.1371/journal.pdig.0000052},
volume = {1},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The time-varying reproduction number (Rt) is an important measure of transmissibility during outbreaks. Estimating whether and how rapidly an outbreak is growing (Rt > 1) or declining (Rt < 1) can inform the design, monitoring and adjustment of control measures in real-time. We use a popular R package for Rt estimation, EpiEstim, as a case study to evaluate the contexts in which Rt estimation methods have been used and identify unmet needs which would enable broader applicability of these methods in real-time. A scoping review, complemented by a small EpiEstim user survey, highlight issues with the current approaches, including the quality of input incidence data, the inability to account for geographical factors, and other methodological issues. We summarise the methods and software developed to tackle the problems identified, but conclude that significant gaps remain which should be addressed to enable easier, more robust and applicable estimation of Rt during epidemics.
AU  - Nash,RK
AU  - Nouvellet,P
AU  - Cori,A
DO  - 10.1371/journal.pdig.0000052
EP  - 0000052
PY  - 2022///
SN  - 2767-3170
SP  - 0000052
TI  - Real-time estimation of the epidemic reproduction number: Scoping review of the applications and challenges
T2  - PLOS Digital Health
UR  - http://dx.doi.org/10.1371/journal.pdig.0000052
UR  - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000052
UR  - http://hdl.handle.net/10044/1/98030
VL  - 1
ER  -
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