Journal article
Practical considerations for measuring the effective reproductive number, Rt
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.9MB, Terms of use)
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(Preview, Corrected version of record, pdf, 428.5KB, Terms of use)
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- Publisher copy:
- 10.1371/journal.pcbi.1008409
Authors
- Publisher:
- Public Library of Science
- Journal:
- PLoS Computational Biology More from this journal
- Volume:
- 16
- Issue:
- 12
- Article number:
- e1008409
- Publication date:
- 2020-12-10
- Acceptance date:
- 2020-12-01
- DOI:
- EISSN:
-
1553-7358
- ISSN:
-
1553-734X
- Pmid:
-
33301457
- Language:
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English
- Keywords:
- Pubs id:
-
1150053
- Local pid:
-
pubs:1150053
- Deposit date:
-
2021-01-15
- ARK identifier:
Terms of use
- Copyright holder:
- Gostic et al.
- Copyright date:
- 2020
- Rights statement:
- © 2020 Gostic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Notes:
- A correction to this article is available online from Public Library of Science at: https://doi.org/10.1371/journal.pcbi.1009679
- Licence:
- CC Attribution (CC BY)
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