Challenges in control of COVID-19: short doubling time and long delay to effect of interventions

  1. Lorenzo Pellis
  2. Francesca Scarabel
  3. Helena B. Stage
  4. Christopher E. Overton
  5. Lauren H. K. Chappell
  6. Elizabeth Fearon
  7. Emma Bennett
  8. Katrina A. Lythgoe
  9. Thomas A. House
  10. Ian Hall
  11. University of Manchester COVID-19 Modelling Group

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Early assessments of the growth rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but as cases were recorded in multiple countries, more robust inferences could be made. Using multiple countries, data streams and methods, we estimated that, when unconstrained, European COVID-19 confirmed cases doubled on average every 3 days (range 2.2–4.3 days) and Italian hospital and intensive care unit admissions every 2–3 days; values that are significantly lower than the 5–7 days dominating the early published literature. Furthermore, we showed that the impact of physical distancing interventions was typically not seen until at least 9 days after implementation, during which time confirmed cases could grow eightfold. We argue that such temporal patterns are more critical than precise estimates of the time-insensitive basic reproduction number R 0 for initiating interventions, and that the combination of fast growth and long detection delays explains the struggle in countries' outbreak response better than large values of R 0 alone. One year on from first reporting these results, reproduction numbers continue to dominate the media and public discourse, but robust estimates of unconstrained growth remain essential for planning worst-case scenarios, and detection delays are still key in informing the relaxation and re-implementation of interventions.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Article activity feed

  1. Version published to 10.1098/rstb.2020.0264
  2. Version published to 10.1101/2020.04.12.20059972v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.12.20059972: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    S1 to S3 Table S1 Other Supplementary Materials for this manuscript include the following: Data sources and the code used to carry out our data analysis can be found at: https://github.com/thomasallanhouse/covid19-growth Materials and Methods:
    Methods
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.04.12.20059972v1 on medRxiv