Next weeks of SARS-CoV-2: Projection model to predict time evolution scenarios of accumulated cases in Spain

  1. Antonio Monleon-Getino
  2. Jaume Canela-Soler

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background and objectives

SARS-CoV-2 is a new type of coronavirus that can affect people and causes respiratory disease, COVID-19. It is affecting the entire planet and we focus in Spain, where the first case was detected at the end of January 2020 and in recent weeks it has increased in many cases. We need predictive models in order to be efficient and take actions. The general goal of this work is present a new model of SARS-CoV-2 to predict different scenarios of accumulated cases in Spain.

Material and methods

In this short report is used a model proposed previously, based on a parametric model Weibull and in a the library BDSbiost3 developed in R to infer and predict different scenarios of the evolution of SARS-CoV-2 for the accumulated cases in Spain after the spread that affects Spain detected at the end of January of this year.

Results

In the analyses presented, projective curves have been generated for the evolution of accumulated cases in which they reach about 4,000 cases or about 15,000 cases, for which the lines of the day in which the value for 90 will be reached can be seen vertically 90, 95 and 99% of the asymptote (maximum number of cases, from that day they will begin to descend or remain the same), that is why the vertical lines would indicate the brake of the disease. For the worst-case scenario, it takes 118, 126 or 142 days to reach the maximum number of cases (n = 15,000) to reach 90, 95 and 99% of the asymptote (maximum number of cases), respectively. This means translated in a time scale that in the worst case the virus will not stop its progress, in Spain, until summer 2020, hopefully before.

Comments and conclusions

This model could be used to plan the resources and see if the policies or means dedicated to the virus are slowing the progress of the virus or it is necessary to implement others that are more effective, and can also validate a method for future outbreaks of diseases such as these.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.09.20059881: (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: 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 10.1101/2020.04.09.20059881v1 on medRxiv