A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
Open accessAbstract
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
- 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
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › 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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