Understanding COVID-19 Epidemic in Costa Rica Through Network-Based Modeling

As a result of the critical health situation caused by COVID-19, governments and researchers have acknowledged the significance of epidemic models for understanding a transmissible disease and assessing public policies, in order to determine which ones are truly effective in mitigating its propagation. We apply a modified SEIR model to characterize the behavior of the COVID-19 epidemic in the context of Costa Rica, employing a contact network to simulate the social connections among the inhabitants. Then, we use this model to weigh up the impact of important sanitary restrictions by simulating different scenarios associated to vaccination, authorization for organizing social events, and reopening of the school system. Our validation tests show that the obtained model is precise. In the scenario evaluation, simulations estimate that a constant vaccination reduces the reported cases by 45% and deaths by 42% in the best case where the infection dies out. In contrast, opening the schools with the totality of students increases the number of reported cases by 46% and deaths by 39% in the worst case. Finally, our model predicts that allowing social events causes an increase of 24% in reported infections and 17% more deaths, specially if people gather with close contacts.