Modeling the Impact of Public Transportation and Mitigation Strategies in COVID-19 Spread in Mexico City

Understanding the spread of infectious diseases such as COVID-19 has been a persistent challenge for scientists and policymakers. While numerous studies have researched general aspects of the COVID-19 transmission dynamics, few have specifically examined its spread within public transportation systems in highly interconnected urban environments while incorporating the role played by the degree of effectiveness of time-varying mitigation strategies. This paper presents a novel approach using a discrete, probabilistic, and age-structured model that integrates the concept of compartmental models in epidemiology with cellular automata networks to investigate the role of public transportation and mitigation strategies in COVID-19 transmission in Mexico City districts. Therefore, the transmission of the disease is based on the definition of two distinct forces of infection: one associated with the use of public transport and the other with the occurrence within each district. Furthermore, the model incorporates several key time-varying parameters such as district population, commuting patterns, and contact matrices for different settings. Moreover, a stringency index was constructed based on the Oxford index in order to evaluate the impact of mitigation strategies established by the Mexican government and their compliance. Simulation results successfully reproduce COVID-19 transmission dynamics in Mexico City districts before vaccination efforts. Findings suggest that public transportation significantly contributed to the spread of the disease during the initial phase of the pandemic in Mexico City. These insights provide valuable guidance for public health strategies to mitigate infectious disease transmission in densely populated urban environments.