Biological Sciences Division
Published December 10, 2020 | Version v1
Journal article Open

Practical considerations for measuring the effective reproductive number, Rt

Creators

  • 1. University of Chicago
  • 2. London School of Hygiene & Tropical Medicine
  • 3. Harvard University
  • 4. Boston University
  • 5. ETH Zürich
  • 6. Leiden University

Description

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.

Files

journal.pcbi.1008409.pdf

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Additional details

Identifiers

DOI
10.1371/journal.pcbi.1008409
Other
oai:uchicago.tind.io:6207

Funding

James S. McDonnell Foundation
National Institute of General Medical Sciences
F32GM134721
National Institute of General Medical Sciences
Morris-Singer Fund and from Models of Infectious Disease Agent Study cooperative agreement
National Institutes of Health
R01 GM122876
Wellcome Trust
210758/Z/18/Z
Christ Church
Junior Research Fellowship
National Institute of Allergy and Infectious Diseases
HHSN272201400005C

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Ecology and Evolution