American Journal of Epidemiology· 2013Q1
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
- 1,789citations
- Q1SCImago
- 2013year
Short summary
A new, user-friendly framework and software tool, implemented in Microsoft Excel, now allows for statistically robust estimation of time-varying reproduction numbers (R) during epidemics, incorporating uncertainty in serial interval distributions.
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Key points
- Introduces a new framework and software tool for estimating time-varying reproduction numbers (R) during epidemics.
- The tool is implemented in Microsoft Excel, making it accessible to users without extensive statistical modeling backgrounds.
- Provides statistically robust analytical estimates of R, incorporating uncertainty in the serial interval distribution.
- Validated on five historical outbreaks, showing consistent results with existing literature.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. American Journal of Epidemiology, 2013 · DOI ↗
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Field: Modeling and Simulation
Modeling and SimulationMathematics