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-dependent reproduction number globally, regionally, nationally, and subnationally over a 12 week rolling window. Our modelling framework, based on open source tooling, accounts for uncertain reporting delays, so that the reproduction number is estimated based on underlying latent infections and not reported cases or deaths.

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 epidemioligcally 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

For more detail and the limitations of these estimates please see our methods), our preprint (currently being updated)(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 along with the code used to produce them.