Authors: Sam Abbott, Joel Hellewell, Robin N. Thompson, Katharine Sherratt, Hamish P. Gibbs, Nikos I. Bosse, James D. Munday, Sophie Meakin, Emma L. Doughty, June Young Chun, Yung-Wai Desmond Chan, Flavio Finger, Paul Campbell, Akira Endo, Carl A. B. Pearson, Amy Gimma, Tim Russell, CMMID COVID modelling group, Stefan Flasche, Adam J. Kucharski, Rosalind M. Eggo, Sebastian Funk
Published: June, 2020 in Wellcome Open Research
Abstract
Background: Assessing temporal variations in transmission in different countries is essential for monitoring the epidemic, evaluating the effectiveness of public health interventions and estimating the impact of changes in policy.
Methods: We use case and death notification data to generate daily estimates of the time-varying reproduction number globally, regionally, nationally, and subnationally over a 12-week rolling window. Our modelling framework, based on open source tooling, accounts for uncertainty in reporting delays, so that the reproduction number is estimated based on underlying latent infections.
Results: Estimates of the reproduction number, trajectories of infections, and forecasts are displayed on a dedicated website as both maps and time series, and made available to download in tabular form.
Conclusions: This decision-support tool can be used to assess changes in virus transmission both globally, regionally, nationally, and subnationally. This allows public health officials and policymakers to track the progress of the outbreak in near real-time using an epidemiologically valid measure. As well as providing regular updates on our website, we also provide an open source tool-set so that our approach can be used directly by researchers and policymakers on confidential data-sets. We hope that our tool will be used to support decisions in countries worldwide throughout the ongoing COVID-19 pandemic.
Cite
@article{abbott_estimating_2020,
title = {Estimating the time-varying reproduction number of {SARS}-{CoV}-2 using national and subnational case counts},
volume = {5},
issn = {2398-502X},
url = {https://wellcomeopenresearch.org/articles/5-112/v1},
doi = {10.12688/wellcomeopenres.16006.1},
abstract = {Background:
Interventions are now in place worldwide to reduce transmission of the novel coronavirus. Assessing temporal variations in transmission in different countries is essential for evaluating the effectiveness of public health interventions and the impact of changes in policy. Methods: We use case notification data to generate daily estimates of the time-dependent reproduction number in different regions and countries. Our modelling framework, based on open source tooling, accounts for reporting delays, so that temporal variations in reproduction number estimates can be compared directly with the times at which interventions are implemented. Results: We provide three example uses of our framework. First, we demonstrate how the toolset displays temporal changes in the reproduction number. Second, we show how the framework can be used to reconstruct case counts by date of infection from case counts by date of notification, as well as to estimate the reproduction number. Third, we show how maps can be generated to clearly show if case numbers are likely to decrease or increase in different regions. Results are shown for regions and countries worldwide on our website (https://epiforecasts.io/covid/) and are updated daily. Our tooling is provided as an open-source R package to allow replication by others. Conclusions: This decision-support tool can be used to assess changes in virus transmission in different regions and countries worldwide. This allows policymakers to assess the effectiveness of current interventions, and will be useful for inferring whether or not transmission will increase when interventions are lifted. As well as providing daily updates on our website, we also provide adaptable computing code so that our approach can be used directly by researchers and policymakers on confidential datasets. We hope that our tool will be used to support decisions in countries worldwide throughout the ongoing {COVID}-19 pandemic.},
pages = {112},
journaltitle = {Wellcome Open Research},
shortjournal = {Wellcome Open Res},
author = {Abbott, Sam and Hellewell, Joel and Thompson, Robin N. and Sherratt, Katharine and Gibbs, Hamish P. and Bosse, Nikos I. and Munday, James D. and Meakin, Sophie and Doughty, Emma L. and Chun, June Young and Chan, Yung-Wai Desmond and Finger, Flavio and Campbell, Paul and Endo, Akira and Pearson, Carl A. B. and Gimma, Amy and Russell, Tim and {CMMID COVID modelling group} and Flasche, Stefan and Kucharski, Adam J. and Eggo, Rosalind M. and Funk, Sebastian},
urldate = {2020-08-27},
date = {2020-06-01},
langid = {english},
file = {Estimating the time-varying reproduction... | Wellcome Open Research:/Users/hamishgibbs/Zotero/storage/8IEZ4WMM/5-112.html:text/html;Full Text:/Users/hamishgibbs/Zotero/storage/LXHRS8X8/Abbott et al. - 2020 - Estimating the time-varying reproduction number of.pdf:application/pdf},
}