An novel epidemiological model for COVID-19

  1. Mauro Gaspari

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Abstract

COVID-19 is characterized by a large number of asymptomatic and mild cases that are difficult to detect; most of them remain unknown, still having an important role in the transmission of the disease, this make the pandemic difficult to control. The purpose of this research is to develop an epidemiological model that allow to estimate the number of unknown/asymptomatic cases in a given area.

The SEIAMPR system, a novel simulation based model for COVID-19 is designed and implemented in Python. The intuition of the model is simple: about 80% of COVID-19 infected people evolve as asymptomatic or with a mild clinical course, many of them remain unknown to the authorities, some of them including those in critical conditions are eventually detected and classified as positive cases. The simulator reproduces this process using an adaptive method integrated with official data.

The simulator has been used for modelling the outbreak in 21 regions in Italy. The positive effects of lockdown policies are demonstrated: unknown active cases 12 days after the lockdown (March the 21th) ranged from 284101 to 374038, e.g. many more than all the official cases in Italy, reducing to 10213/20949 the reopening day. The number of unknown active cases at the beginning of June in the Lombardia region ranged from 6813 to 13390 demanding particular attention.

SEIAMPR is simple to tune and integrate with official data, it emerges as an up-and-coming tool for reporting the effect of lockdown measures, the impact of the disease on the population, and the remaining unknown active cases for evaluating the timing of exit strategies.

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  1. Version published to 10.1101/2020.07.23.20160580v2 on medRxiv
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    SciScore for 10.1101/2020.07.23.20160580: (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: Thank you for sharing your code and data.

    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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  3. Version published to 10.1101/2020.07.23.20160580v1 on medRxiv