Quantifying Inaccuracies in Modeling COVID-19 Pandemic within a Continuous Time Picture

  1. Ioan Bâldea

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Abstract

Typically, mathematical simulation studies on COVID-19 pandemic forecasting are based on deterministic differential equations which assume that both the number ( n ) of individuals in various epidemiological classes and the time ( t ) on which they depend are quantities that vary continuous. This picture contrasts with the discrete representation of n and t underlying the real epidemiological data reported in terms daily numbers of infection cases, for which a description based on finite difference equations would be more adequate. Adopting a logistic growth framework, in this paper we present a quantitative analysis of the errors introduced by the continuous time description. This analysis reveals that, although the height of the epidemiological curve maximum is essentially unaffected, the position obtained within the continuous time representation is systematically shifted backwards in time with respect to the position predicted within the discrete time representation. Rather counterintuitively, the magnitude of this temporal shift is basically insensitive to changes in infection rate κ . For a broad range of κ values deduced from COVID-19 data at extreme situations (exponential growth in time and complete lockdown), we found a rather robust estimate τ ≃ −2.65 day −1 . Being obtained without any particular assumption, the present mathematical results apply to logistic growth in general without any limitation to a specific real system.

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    SciScore for 10.1101/2020.09.05.20188755: (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).

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.09.05.20188755v1 on medRxiv