Modelling COVID-19 in Brazil: better fit to data obtained when including the percolation effect approximation

The SARS-CoV-2 pandemic has had an unprecedented impact on multiple levels of society. Not only has the pandemic completely overwhelmed some health systems but it has also changed how scientific evidence is shared and increased the pace at which such evidence is published and consumed, by scientists, policymakers and the wider public. Through the COVID-19 Modelling (CoMo) Consortium and the Observatório COVID-19 BR, we created modelling frameworks that could help simulate the effect of different non-pharmaceutical interventions on mitigating the epidemic in numerous locations. Here, we describe how this framework was adapted to the Brazilian context and, more specifically, fitted to data from the city of Sao Paulo. We propose an approximation for the percolation effect observed in social networks connectivity, due to the adoption of social distancing measures, and we show that this leads to better fitting to data, indicating the importance of this effect in such a system.