A Markov Chain Model for COVID19 in Mexico City

TL;DR

This paper develops a discrete-time Markov chain model to analyze the progression of COVID-19 in Mexico City, estimating state transition probabilities from susceptible to death using government data.

Contribution

It introduces a Markov chain approach specifically tailored to Mexico City's COVID-19 data, accounting for underreporting and providing insights into disease progression.

Findings

Model approximates pandemic evolution effectively.

Provides estimates of transition probabilities between health states.

Highlights underestimation issues due to testing limitations.

Abstract

This paper presents a model for COVID19 in Mexico City. The data analyzed were considered from the appearance of the first case in Mexico until July 2021. In this first approximation the states considered were Susceptible, Infected, Hospitalized, Intensive Care Unit, Intubated, and Dead. As a consequence of the lack of coronavirus testing, the number of infected and dead people is underestimated, although the results obtained give a good approximation to the evolution of the pandemic in Mexico City. The model is based on a discrete-time Markov chain considering data provided by the Mexican government, the main objective is to estimate the transient probabilities from one state to another for the Mexico City case.