An epidemic model for economical impact predicting and spatiotemporal spreading of COVID-19

  1. Mateo Cámara
  2. Mario Miravete
  3. Eduardo Navarro

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Abstract

Since the emergence of a new strain of coronavirus known as SARS-CoV-2, many countries around the world have reported cases of COVID-19 disease caused by this virus. Numerous people’s lives have been affected both from a health and an economic point of view. The long tradition of using mathematical models to generate insights about the transmission of a disease, as well as new computer techniques such as Artificial Intelligence, have opened the door to diverse investigations providing relevant information about the evolution of COVID-19. In this research, we seek to advance the existing epidemiological models based on microscopic Markov chains to predict the impact of the pandemic at medical and economic levels. For this purpose, we have made use of the Spanish population movements based on mobile-phone geographically-located information to determine its economic activity using Artificial Intelligence techniques and have developed a novel advanced epidemiological model that combines this information with medical data. With this tool, scenarios can be released with which to determine which restriction policies are optimal and when they have to be applied both to limit the destruction of the economy and to avoid the feared possible upsurge of the disease.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.02.20186551: (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

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  2. Version published to 10.1101/2020.09.02.20186551v1 on medRxiv