The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease

  1. Giuseppe De Natale
  2. Valerio Ricciardi
  3. Gabriele De Luca
  4. Dario De Natale
  5. Giovanni Di Meglio
  6. Antonio Ferragamo
  7. Vito Marchitelli
  8. Andrea Piccolo
  9. Antonio Scala
  10. Renato Somma
  11. Emanuele Spina
  12. Claudia Troise

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Abstract

We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of the number of infection cases. To give a more realistic estimate of the lethality of COVID-19, we use the actual (March 2020) estimates of the infection fatality ratio (IFR) of the pandemic based on the minimum observed CFR and analyze data obtained from the Diamond Princess cruise ship, a good representation of a “laboratory” case-study from an isolated system in which all the people have been tested. From such analyses, we derive more realistic estimates of the real extent of the infection as well as more accurate indicators of how fast the infection propagates. We then isolate the dominant factors causing the abnormal severity of the disease in Italy. Finally, we use the death count—the only data estimated to be reliable enough—to predict the total number of people infected and the interval of time when the infection in Italy could end.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.28.20046243: (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 10.3390/jcm9051564
  3. Version published to 10.1101/2020.03.28.20046243v2 on medRxiv
  4. Version published to 10.20944/preprints202004.0049.v1
  5. Version published to 10.1101/2020.03.28.20046243v1 on medRxiv