A data-driven epidemiological model to explain the Covid-19 pandemic in multiple countries and help in choosing mitigation strategies

Beira, M. J.; Kumar, A.; Perfeito, L.; Goncalves-Sa, J.; Sebastiao, P. J.

Accurate models are fundamental to understand the dynamics of the COVID-19 pandemic and to evaluate different mitigation strategies. Here, we present a multi-compartmental model that fits the epidemiological data for eleven countries, despite the reduced number of fitting parameters. This model consistently explains the data for the daily infected, recovered, and dead over the first six months of the pandemic. The good quality of the fits makes it possible to explore different scenarios and evaluate the impact of both individual and collective behaviors and government- level decisions to mitigate the epidemic. We identify robust alternatives to lockdown, such as self- protection measures, and massive testing. Furthermore, communication and risk perception are fundamental to modulate the success of different strategies. The fitting/simulation tool is publicly available for use and test of other models, allowing for comparisons between different underlying assumptions, mitigation measures, and policy recommendations.