Double Power Law for COVID-19: Prediction of New Cases and Death Rates in Italy and Spain

  1. Vladimir A. Osherovich
  2. Joseph Fainberg
  3. Lev Z. Osherovich

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

1.

The novel corona virus SARS-CoV-2 appeared at the end of 2019, spreading rapidly and causing a severe respiratory syndrome (COVID-19) with high mortality (2-5%). Until a vaccine or therapy is found, the most effective method of prophylaxis has been to minimize transmission via rigorous social distancing and seclusion of all but essential workers. Such measures, implemented at different times and to varying degrees world-wide, have reduced the rate of transmission compared with early phases of the pandemic, resulting in “flattening of the curve” followed by a gradual reduction in mortality after >6 weeks of rigorous social distancing measures. The cost of rigorous social distancing has been seen in radically reduced economic activity, job losses, disruption of schooling and social institutions. A key question facing policy makers and individuals is when to resume normal economic and social activity in the face of persistent community transmission of SARS-CoV-2. To help address this question, we have developed a model that accurately describes the entire transmission and mortality curves in Italy and Spain, two hard-hit countries that have maintained severe social distancing measures for over 2 months. Our model quantitatively describes the rapid rise and slow decay of new cases and deaths observed under stringent social distancing (the “long tail” effect). We predict that even when social distancing is rigorously maintained, the number of COVID-19 deaths after peak mortality may be 2 – 3 times larger than the total number of deaths up to the peak. Our model has important policy implications for countries currently debating how to ease social distancing measures.

Article activity feed

  1. Version published to 10.1101/2020.05.07.20094714v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.05.07.20094714: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The observed slow decay of case numbers even after stringent social distancing measures reveals limitations to non-pharmaceutical interventions to control transmission. The DPL model predicts that even if social distancing is rigorously maintained, the number of COVID-19 deaths after DR peak may be 2 – 3 times larger than the total number of deaths up to the peak. Second, the DPL model should inform rational decision-making about when to ease social distancing measures. As many countries are now relaxing their social distancing measures, we expect that the DPL model could be used to parametrize and compare the effect of different policies on NC and DR. Premature slackening of social distancing measures is expected to cause resumption of growth phase, resulting in increased cases and deaths, but it remains unclear when there is unlikely to be further benefit to continue with social distancing. The DPL model’s predictive utility diminishes as NC and DR approach the root mean square deviation of observed data, given as Sigma in Table 1. However, if social distancing measures are withdrawn before NC has reached Sigma, the DPL model predicts that growth will resume according to the first power law component, although the impact of resumed growth on NC would not be apparent for at least two weeks. Likewise, when new vaccines and therapies are developed and deployed, changes in the parameters of the DPL model can be used measure the impact of these interventions on NC and DR. We int...

    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.

  3. Version published to 10.1101/2020.05.07.20094714v1 on medRxiv