Research of Adequacy of Models and SIRS Based on Cellular Automata

10 Pages Posted: 10 Aug 2020

See all articles by Dmitry Lande

Dmitry Lande

Institute for information Recording of NASU

Leonard Strashnoy

University of California, Los Angeles (UCLA)

Date Written: July 28, 2020

Abstract

This paper describes a comparison of the SIRS model, built on the basis of cellular automata, and the real statistics of daily mortality in the course of the COVID-19 pandemic in different countries. The SIRS model is used with different parameters of infectability and loss of susceptibility. The model calculates the cross-correlation of mortality dynamics with the real dynamics of mortality in different countries. A high correlation (more than 0.5) of mortality dynamics in all countries with the dynamics given by the model at the selected parameters is shown.

The research results allow us to fix the model parameters that can further perform forecasting. The advantage of the SIRS model based on cellular automata is the simplicity and clarity of a small number of parameters, and the ability to change them in accordance with epidemiological data. The model demonstrates the fact that the daily mortality rate is eventually reduced to zero, although under different parameters (primarily the probability of loss of immunity), the system can survive several waves of infection.

Note: Funding: None to declare

Declaration of Interest: None to declare

Keywords: COVID-19, pandemic, modeling, SIRS, cross-correlation, cellular automata, datasets

Suggested Citation

Lande, Dmitry and Strashnoy, Leonard, Research of Adequacy of Models and SIRS Based on Cellular Automata (July 28, 2020). Available at SSRN: https://ssrn.com/abstract=3662821 or http://dx.doi.org/10.2139/ssrn.3662821

Dmitry Lande (Contact Author)

Institute for information Recording of NASU ( email )

Shpaka str., 2
Kyiv, 03013
Ukraine

Leonard Strashnoy

University of California, Los Angeles (UCLA) ( email )

405 Hilgard Avenue
Box 951361
Los Angeles, CA 90095
United States

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