Covid-19 Incidence Rate Evolution Modeling using Dual Wave Gaussian-Lorentzian Composite Functions

  1. Radhakrishnan Poomari

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Abstract

Modeling the evolution of Covid-19 incidence rate is critical to deciding and assessing non-medical intervention strategies that can lead to successful containment of the pandemic. This research presents a mathematical model to empirically assess measures related to various pandemic containment strategies, their similarities and a probabilistic estimate on the evolution of Covid-19 incidence rates. The model is built on the principle that, the exponential rise and decay of the number of confirmed Covid-19 infections can be construed as a set of concurrent non-linear waves. These waves can be characterized by a linear combination of Gaussian and Cauchy Lorentz functions collectively termed as Gaussian-Lorentzian Composite (GLC) function. The GLC function is used for non-linear approximation of officially confirmed Covid-19 incidence rates in each country. Results of fitting GLC based models to incidence rate trends of 20 different countries proves that the models can empirically explain the growth and decay trajectory Covid-19 infections in a given population.

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    SciScore for 10.1101/2020.06.07.20124966: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

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    Table 2: Resources

    Experimental Models: Organisms/Strains
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    The scaling factor creates an inverse relationship between h1 and h2.
    h1
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  2. Version published to 10.1101/2020.06.07.20124966v1 on medRxiv