Removing weekly administrative noise in the daily count of COVID-19 new cases. Application to the computation of Rt

  1. Luis Alvarez
  2. Miguel Colom
  3. Jean-Michel Morel

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The way each country counts and reports the incident cases of SARS-CoV-2 infections is strongly affected by the “weekend effect”. During the weekend, fewer tests are carried out and there is a delay in the registration of cases. This introduces an “administrative noise” that can strongly disturb the calculation of trend estimators such as the effective reproduction number R ( t ). In this work we propose a procedure to correct the incidence curve and obtain a better fit between the number of infected and the one expected using the renewal equation. The classic way to deal with the administrative noise is to invoke its weekly period and therefore to filter the incidence curve by a seven days sliding mean. Yet this has three drawbacks: the first one is a loss of resolution. The second one is that a 7-day mean filter hinders the estimate of the effective reproduction number R ( t ) in the last three days before present. The third drawback of a mean filter is that it implicitly assumes the administrative noise to be additive and time invariant. The present study supports the idea that the administrative is better dealt with as being both periodic and multiplicative. The simple method that derives from these assumptions amount to multiplying the number of infected by a correcting factor which depends on the day of the week. This correcting factor is estimated from the incidence curve itself. The validity of the method is demonstrated by its positive impact on the accuracy of an the estimates of R ( t ). To exemplify the advantages of the multiplicative periodic correction, we apply it to Sweden, Germany, France and Spain. We observe that the estimated administrative noise is country dependent, and that the proposed strategy manages to reduce it noise considerably. An implementation of this technique is available at www.ipol.im/ern , where it can be tested on the daily incidence curves of an extensive list of states and geographic areas provided by the European Centre for Disease Prevention and Control.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.16.20232405: (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

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

  2. Version published to 10.1101/2020.11.16.20232405v1 on medRxiv