Stochastic epidemiological model: Modeling the SARS-CoV-2 spreading in Mexico

TL;DR

This paper introduces a stochastic epidemiological model for SARS-CoV-2 spread in Mexico, utilizing a LI(RD) compartmental structure with Poisson-distributed new infections modulated by a time-dependent weight function, enabling analysis of periodic patterns in data.

Contribution

The paper presents a novel stochastic model based on first principles with a time-dependent infection rate, allowing for self-consistent analysis of epidemiological data and periodic patterns.

Findings

Model captures temporal variations in infection rates.

Allows extraction of periodic patterns from empirical data.

Provides a self-consistent framework for epidemic analysis.

Abstract

In this paper we model the spreading of the SARS-CoV-2 in Mexico by introducing a new stochastic approximation constructed from first principles, structured on the basis of a Latent-Infectious- (Recovered or Deceased) (LI(RD)) compartmental approximation, where the number of new infected individuals caused by a single infectious individual per unit time (a day), is a random variable of a Poisson distribution and whose parameter is modulated through a weight-like time-dependent function. The weight function serves to introduce a time dependence to the average number of new infections and as we will show, this information can be extracted from empirical data, giving to the model self-consistency and provides a tool to study information about periodic patterns encoded in the epidemiological dynamics