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 been facing many issues due to the viral spread. Even in cities with high seroprevalence, intense resurgent waves of COVID-19 have been registered, possibly due to viral variants with greater transmission rates. Accordingly, we develop a new Model Predictive Control (MPC) framework that is able to determine social distancing guidelines and altogether provide estimates for the future epidemiological characteristic of the contagion. For such, the viral dynamics are represented through a Linear Parameter Varying (LPV) version of the Susceptible-Infected-Recovered-Deceased (SIRD) model. The solution of the LPV MPC problem is based on a Sequential Quadratic Program (SQP). This SQP provides convergent estimates of the future LPV scheduling parameters. We use real data to illustrate the efficiency of the proposed method to mitigate this contagion while vaccination is ongoing.