Cited by 1,530COVID-19 epidemiological studiesMathematical and Theoretical Epidemiology and Ecology ModelsData-Driven Disease Surveillance

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A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

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Cori, Anne, Ferguson, Neil M., Fraser, Christophe, Cauchemez, Simon·2013·American Journal of Epidemiology

DOI 10.1093/aje/kwt133PMC PMC3816335

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Abstract

The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.

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Authors: Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S.
Affiliations: Imperial College London (GB)
University of Oxford (GB)
Institut Pasteur (FR)
Corresponding author: Cori, Anne
ORCID: Cori, Anne
Ferguson, Neil M.
Fraser, Christophe
Cauchemez, Simon
Venue: American Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 2013-09-15
Volume: 178
Issue: 9
Pages: 1505-1512
ISSN: 0002-9262 (ISSN-L)
1476-6256
DOI: 10.1093/aje/kwt133
PMC: PMC3816335
OpenAlex: W2097446414
Cited by: 1,530
References: 45
Citations by year: 2026: 94 · 2025: 141 · 2024: 161 · 2023: 199 · 2022: 268 · 2021: 328 · 2020: 262 · 2019: 21 · 2018: 16 · 2017: 16 · 2016: 16 · 2015: 3 · 2014: 4 · 2013: 1
FWCI: 8.33
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: American Journal of Epidemiology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Royal Society
Imperial College London
Medical Research Council (MR/K010174/1, G0800596)
National Institute of General Medical Sciences
Topics: Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Keywords: time-varying reproduction number, analytical estimation, transmission intensity, public health response
MeSH terms: Humans, Software, Time Factors, Virus Diseases, Models, Statistical, Incidence, Epidemics

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A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

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