In this work, we propose a 2D lattice gas model for infection spreading, and we apply it to study the COVID-19 pandemic in the Mexico City Metropolitan Area (MCMA). We compared the spatially averaged results of this model against the MCMA available data. With the model, we estimated the numbers of daily infected and dead persons and the epidemic's duration in the MCMA. In the simulations, we included the small-world effects and the impact of lifting/strengthen lockdown measures. We included some indicators of the goodness of fit; in particular, the Pearson correlation coefficient resulted larger than 0.9 for all the cases we considered. Our modeling approach is a research tool that can help assess the effectiveness of strategies and policies to address the pandemic phenomenon and its consequences.
Keywords: COVID-19; Data-driven; Lattice gas; Mexico City Metropolitan Area; Pandemic evolution.
© 2020 The Author(s).