Practical considerations for measuring the effective reproductive number, R t
Abstract
Abstract Estimation of the effective reproductive number, R t , is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using R t to assess the effectiveness of interventions and to inform policy. However, estimation of R t 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 R t , we recommend the approach of Cori et al. (2013), 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 (2004), 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 spread. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting R t 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 R t estimation. Author summary The effective reproductive number, R t , is a key epidemic parameter used to assess whether an epidemic is growing, shrinking or holding steady. R t estimates can be used as a near real-time indicator of epidemic growth or to assess the effectiveness of interventions. But due to delays between infection and case observation, estimating R t in near real-time, and correctly inferring the timing of changes in R t is challenging. Here, we provide an overview of challenges and best practices for accurate, timely R t estimation.
Authors 25
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Katelyn M. Gostic corresponding
Affiliation as printed
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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Affiliation as printed
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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Affiliation as printed
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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University of Oxford · London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
Mathematical Institute, University of Oxford, Oxford, UK
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Affiliation as printed
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
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Affiliation as printed
Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
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SIB Swiss Institute of Bioinformatics · ETH Zurich
Affiliation as printed
Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
Swiss Institute of Bioinformatics, Basel, Switzerland
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Affiliation as printed
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
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SIB Swiss Institute of Bioinformatics · ETH Zurich
Affiliation as printed
Department of Biosystems Science and Engineering, ETH Zürich, Switzerland
Swiss Institute of Bioinformatics, Basel, Switzerland
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Leiden University · Leiden University Medical Center · National Institute for Public Health and the Environment
Affiliation as printed
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Affiliation as printed
Center for Communicable Disease Dynamics, Department of Epidemiology, T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA
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Affiliation as printed
Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
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