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Are epidemic growth rates more informative than reproduction numbers?

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  • Kris V. Parag
  • Robin N. Thompson
  • Christl A. Donnelly

Abstract

Summary statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, Rt, is predominant among these statistics, measuring the average ability of an infection to multiply. However, Rt encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, rt, that is, the rate of change of the log‐transformed case incidence, as a more temporally meaningful and model‐agnostic policy guide. We examine this assertion, identifying if and when estimates of rt are more informative than those of Rt. We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding public health interventions in real time.

Suggested Citation

  • Kris V. Parag & Robin N. Thompson & Christl A. Donnelly, 2022. "Are epidemic growth rates more informative than reproduction numbers?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 5-15, November.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:s1:p:s5-s15
    DOI: 10.1111/rssa.12867
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    References listed on IDEAS

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    1. Luís M A Bettencourt & Ruy M Ribeiro, 2008. "Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases," PLOS ONE, Public Library of Science, vol. 3(5), pages 1-9, May.
    2. J. O. Lloyd-Smith & S. J. Schreiber & P. E. Kopp & W. M. Getz, 2005. "Superspreading and the effect of individual variation on disease emergence," Nature, Nature, vol. 438(7066), pages 355-359, November.
    3. Kris V Parag & Christl A Donnelly & Rahul Jha & Robin N Thompson, 2020. "An exact method for quantifying the reliability of end-of-epidemic declarations in real time," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-21, November.
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    Cited by:

    1. Peter J. Diggle & Sylvia Richardson, 2022. "‘Introduction’," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 3-4, November.
    2. Frank Ball & Peter Neal, 2025. "Fast likelihood calculations for emerging epidemics," Statistical Inference for Stochastic Processes, Springer, vol. 28(1), pages 1-25, April.
    3. Lorenzo Pellis & Paul J. Birrell & Joshua Blake & Christopher E. Overton & Francesca Scarabel & Helena B. Stage & Ellen Brooks‐Pollock & Leon Danon & Ian Hall & Thomas A. House & Matt J. Keeling & Jon, 2022. "Estimation of reproduction numbers in real time: Conceptual and statistical challenges," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 112-130, November.
    4. Nicholas P. Jewell & Joseph A. Lewnard, 2022. "On the use of the reproduction number for SARS‐CoV‐2: Estimation, misinterpretations and relationships with other ecological measures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 16-27, November.

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