  The COVID-19 pandemic is the profoundest health crisis of the 21rst century.
The SARS-CoV-2 virus arrived in Brazil around March, 2020 and its social and
economical backlashes are catastrophic. In this paper, it is investigated how
Model Predictive Control (MPC) could be used to plan appropriate social
distancing policies to mitigate the pandemic effects in Bahia and Santa
Catarina, two states of different regions, culture, and population demography
in Brazil. In addition, the parameters of
Susceptible-Infected-Recovered-Deceased (SIRD) models for these two states are
identified using an optimization procedure. The control input to the process is
a social isolation guideline passed to the population. Two MPC strategies are
designed: a) a centralized MPC, which coordinates a single control policy for
both states; and b) a decentralized strategy, for which one optimization is
solved for each state. Simulation results are shown to illustrate and compare
both control strategies. The framework serves as guidelines to deals with such
pandemic phenomena.
