What variables can better predict the number of infections and deaths worldwide by SARS-CoV-2? Variation through time

  1. J. G. García de Alcañíz
  2. J. Romero-López
  3. R. P. Martínez-Esteban
  4. V. López-Rodas
  5. E. Costas

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Using data from 50 very different countries (which represent nearly 70% of world’s population) and by means of a regression analysis, we studied the predictive power of different variables (mobility, air pollution, health & research, economic and social & geographic indicators) over the number of infected and dead by SARS-CoV-2. We also studied if the predictive power of these variables changed during a 4 months period (March, April, May and June). We approached data in two different ways, cumulative data and non-cumulative data.

The number of deaths by Covid-19 can always be predicted with great accuracy from the number of infected, regardless of the characteristics of the country.

Inbound tourism emerged as the variable that best predicts the number of infected (and, consequently, the number of deaths) happening in the different countries. Electricity consumption and air pollution of a country (CO 2 emissions, nitrous oxide and methane) are also capable of predicting, with great precision, the number of infections and deaths from Covid-19. Characteristics such as the area and population of a country can also predict, although to a lesser extent, the number of infected and dead. All predictive variables remained significant through time.

In contrast, a series of variables, which in principle would seem to have a greater influence on the evolution of Covid-19 (hospital bed density, Physicians per 1000 people, Researches in R & D, urban population…), turned out to have very little - or none- predictive power.

Our results explain why countries that opted for travel restrictions and social withdrawal policies at a very early stage of the pandemic outbreak, obtained better results. Preventive policies proved to be the key, rather than having large hospital and medical resources.

Article activity feed

  1. Version published to 10.1101/2020.06.04.20122176v3 on medRxiv
  2. Version published to 10.1101/2020.06.04.20122176v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.04.20122176: (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.

  4. Version published to 10.1101/2020.06.04.20122176v1 on medRxiv