Covid-19 pandemic and main comorbidities in Mexican population a complex network approach

Abstract

Quarantine and vaccination policies are among the most effective mechanisms used to mitigate the spread of infectious diseases. Numerous epidemic models are proposed in the literature. However, in order to produce more realistic scenarios regarding the transmission and infection processes, epidemic models should take into account the interaction among the individuals in combination with specific characteristics like age and health. In this sense, we propose a $SVIQRS$ epidemic model that implements particular characteristics from the Mexican population, like the age distribution and the percentage of people suffering from a chronic illness like high blood pressure, obesity, and diabetes. Our model is capable of reproducing some available statistics from the last Covid-19 pandemic in México, simulating the disease spread in a complex network with degree distribution $P\left(k\right)\sim k^{-2.5}$. In particular, it reproduces closely the distributions of Covid-19 infected individuals and infected individuals who also suffer from diabetes. Additionally, our model indicates through stochastic simulations that the isolation and vaccination of people suffering chronic diseases is an appropriate strategy to prevent the increase in infected individuals and avoid the overloading of health systems. Following this approach, the number of infected individuals grows slowly, preventing the overloading of health systems.