Data-Driven Mathematical Modeling Approaches for COVID-19: a survey

TL;DR

This survey reviews data-driven mathematical modeling methods for COVID-19, covering phenomenological, multi-wave, and multi-compartmental models, based on an analysis of 230 articles across various aspects of the pandemic.

Contribution

It provides a comprehensive overview of modeling approaches for COVID-19, highlighting recent developments and categorizing methods across different epidemic phases and structures.

Findings

Extensive review of 230 articles on COVID-19 modeling.

Classification of models for single and multiple epidemic waves.

Insights into the application of models for forecasting and understanding COVID-19 dynamics.

Abstract

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 230 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population.