Social Interaction Layers in Complex Networks for the Dynamical Epidemic Modeling of COVID-19 in Brazil.

We are currently living in a state of uncertainty due to the pandemic caused by the Sars-CoV-2 virus. There are several factors involved in the epidemic spreading such as the individual characteristics of each city/country. The true shape of the epidemic dynamics is a large, complex system such as most of the social systems. In this context, complex networks are a great candidate due to its ability to tackle structural and dynamical properties. Therefore this study presents a new approach to model and characterizes the COVID-19 epidemic using a multi-layer complex network, where nodes represent people, edges are social contacts and layers represent different social activities. The model improves the traditional SIR and it is applied to study the Brazilian epidemic by analyzing possible future actions and their consequences. The network is characterized by considering statistics of infection, death, and hospitalization time. To simulate isolation, social distancing, or precautionary measures we remove layers and/or reduce the intensity of social contacts. Results show that even taking various optimistic assumptions, the current isolation levels in Brazil still may lead to a collapse of the healthcare system and a considerable death toll (average of 168,000). If all activities return to normal, the epidemic growth suffers a steep increase over the current pattern, and the demand for ICU beds my surpass 3.5 times the country's capacity. This would surely lead to a catastrophic scenario, as our estimation reaches an average of 240,000 deaths even considering that all cases are effectively treated. The increase of isolation (up to a lockdown) shows to be the best option to keep the situation under the healthcare system capacity, aside from ensuring a faster decrease of new case occurrences (months of difference), and a significantly smaller death toll (average of 79,000).