COVID-19 dynamic model: Balanced identification of general biological and country specific social features

TL;DR

This paper introduces a balanced identification method to model COVID-19 dynamics, capturing both universal biological interactions and country-specific social factors using only new case data from official sources.

Contribution

It presents a novel data processing approach that integrates biological laws and social features into a comprehensive COVID-19 model using minimal data.

Findings

Identified general biological laws of virus-human interaction

Quantified country-specific social features affecting epidemic spread

Provided lower estimates of undetected infected cases

Abstract

Breaking a complex bio-social phenomenon (epidemic) into its components, considering the processes that determine its dynamics, formalizing the accepted hypotheses in mathematical equations, selecting appropriate experimental and statistical material, and constructing a mathematical model - those are typical tasks of scientific research. A specific data processing method (balanced identification) and appropriate information technology made it possible to consider a number of models, determine the general biological laws of the virus-human interaction (common to all populations), and the country specific social features of epidemic management in the countries (or cities) under consideration. As the initial data, only new cases are used. Data from different countries is taken from official sources and processed in a uniform way. The obtained estimates of the number of undetected infected are lower estimates.