On the assessment of more reliable COVID-19 infected number: the italian case

  1. Giuseppe Tradigo
  2. Pietro Hiram Guzzi
  3. Pierangelo Veltri

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Abstract

COVID-19 (SARS-CoV-2) is the most recent pandemic disease the world is currently managing. It started in China at the end of 2019, and it is diffusing throughout Italy, one of the most affected countries, and it is currently spreading through European countries and USA. Patients affected by COVID-19 are identified employing medical swabs applied mainly to (i) citizens with COVID-19 symptoms such as flu or high temperature, or (ii) citizens that had contacts with COVID-19 patients. A percentage of COVID-19 affected patients needs hospitalisation, whereas a portion needs to be treated in Intensive Care Units (ICUs).

Nevertheless, it is a matter of current intuition that COVID-19 infected citizens are more than those detected, and sometime the infection is detected too late. Thus there are many efforts in both tracking people activities as well as diffusing low cost reliable COVID-19 tests for early detection.

Starting from mortality rates of diseases caused by viruses in the same family (e.g. MERS, SARS, H1N1), we study the relations between the number of COVID-19 infections and the number of deaths, through Italian regions. We thus assess several infections being higher than the ones currently measured. We thus focus on the characterisation of the pandemic diffusion by estimating the infected number of patients versus the number of death. We use such an estimated number of infections, to foresee the effects of restriction actions adopted by governments to constrain virus diffusion. We finally think that our model can support the healthcare system to react when COVID-19 is increasing.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.25.20043562: (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.

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  2. Version published to 10.1101/2020.03.25.20043562v1 on medRxiv