Phenomenological dynamics of COVID-19 pandemic: meta-analysis for adjustment parameters

TL;DR

This paper introduces a phenomenological method for analyzing COVID-19 data that adjusts dynamic parameters over time, effectively capturing epidemic evolution without relying on traditional differential equation models.

Contribution

It proposes a novel phenomenological approach that uses time-dependent parameters to fit COVID-19 data, accommodating sub-epidemic events and avoiding reliance on predefined models.

Findings

Accurately fits infection and removal data across eleven countries.

Handles sub-epidemic events effectively.

Provides pandemic evolution without differential equation models.

Abstract

We present a phenomenological procedure of dealing with the COVID--19 data provided by government health agencies of eleven different countries. Instead of using the (exact or approximate) solutions to the SIR (or other) model(s) to fit the data by adjusting the time--independent parameters included in those models, we introduce dynamical parameters whose time--dependence may be phenomenologically obtained by adequately extrapolating a chosen subset of the daily provided data. This phenomenological approach works extremely well to properly adjust the number of infected (and removed) individuals in time, for the countries we consider. Besides, it can handle the sub--epidemic events that some countries may experience. In this way, we obtain the evolution of the pandemic without using any a priori model based on differential equations.