Identifying the complex underlying transmission network of COVID-19 in México from 2020 to 2024

Has been demonstrated that the interaction of individuals in a social network can be modeled using the complex networks theory. In this sense, we believe that knowing at least approximately the underlying network of interaction among the individuals in a society could help to understand the dynamic of diseases transmission among the individuals and elucidate the most effective mechanisms to contain the spread and minimize negative effects like the over-saturation of health systems, such as hospitals and medical clinics. In this work, we study the spread of COVID-19 in Mexico from 2020 to 2024 using the statistical data available in the public health departments of Mexico. Through stochastic simulations we found a complex network that could represent a simplification of the real underlying network of interaction among the Mexican population, in particular we found that the network has degree distribution following a power law P(k) ∼ k −γ with exponent close to γ ≈ 2.5. As mentioned before it could help to understand the spread of future diseases through stochastic simulations using a network structure closer to reality and consequently implement best healthy policies.