A Sequential Quadratic Programming Approach for the Predictive Control of the COVID-19 Spread

The COVID-19 pandemic is the defying crisis of our time. Since mass vaccination has not yet been established, countries still have facing many issues due to viral spread. Even in cities with high seroprevalence, intense resurgent waves registered, possibly variants greater transmission rates. Accordingly, we develop a new Model Predictive Control (MPC) framework that able determine social distancing guidelines and altogether provide estimates for future epidemiological characteristic contagion. For such, dynamics are represented through Linear Parameter Varying (LPV) version Susceptible-Infected-Recovered-Deceased (SIRD) model. solution LPV MPC problem based on Sequential Quadratic Program (SQP). This SQP provides convergent scheduling parameters. We use real data illustrate efficiency proposed method mitigate this contagion while ongoing.