Temporal variation in transmission during the COVID-19 outbreak

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

* contributed equally

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.

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.

Keywords: Covid-19, SARS-CoV-2, surveillance, forecasting, time-varying reproduction number

More information For more detail and the limitations of these estimates please see our methods), our preprint(S Abbott et al. 2020), the documentation of our transmission tracking R package(Sam Abbott, Hellewell, et al. 2020) and the documentation of our R data package (Sam Abbott, Sherratt, et al. 2020). Interactive visualisations are powered by RtD3(Gibbs, Abbott, and Funk 2020). Stored versions of our estimates can be found here and the code used to generate them can be found here.**

Note: Our methodology remains an area of active research so please consider contacting us if interested in using, or evaluating, our approach. As an open-source project we also welcome code and methodology contributions, please see the linked code repositories for details on how to contribute.

Abbott, Sam, Joel Hellewell, Joe Hickson, James Munday, Katelyn Gostic, Peter Ellis, Katharine Sherratt, et al. 2020. “EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters.” - - (-): –. https://doi.org/10.5281/zenodo.3957489.

Abbott, Sam, Katharine Sherratt, Jonnie Bevan, Hamish Gibbs, Joel Hellewell, James Munday, Patrick Barks, Paul Campbell, Flavio Finger, and Sebastian Funk. 2020. “Covidregionaldata: Subnational Data for the Covid-19 Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3957539.

Abbott, S, J Hellewell, RN Thompson, K Sherratt, HP Gibbs, NI Bosse, JD Munday, et al. 2020. “Estimating the Time-Varying Reproduction Number of Sars-Cov-2 Using National and Subnational Case Counts.” Wellcome Open Research 5 (112). https://doi.org/10.12688/wellcomeopenres.16006.1.

Gibbs, Hamish, Sam Abbott, and Sebastian Funk. 2020. “RtD3: Rt Visualization in D3.” Zenodo - (-): –. https://doi.org/10.5281/zenodo.4011841.

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Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/epiforecasts/covid, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".