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

机译：一个顺序二次编程方法，用于预测Covid-19扩散

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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.
机译：Covid-19大流行是我们时代的荒谬危机。由于尚未建立大众疫苗接种，因此由于病毒传播，各国仍然面临着许多问题。即使在具有高精度的城市，Covid-19的强烈复苏波也已经注册，可能是由于具有更高传输速率的病毒变体。因此，我们开发了一种新的模型预测控制（MPC）框架，能够确定社交偏移指南，并完全提供了对传染的未来流行病学特征的估计。对于这样，病毒动力学通过敏感感染回收的死亡（SIRD）模型的线性参数变化（LPV）版本表示。 LPV MPC问题的解决方案基于顺序二次程序（SQP）。该SQP提供了未来LPV调度参数的会聚估计。我们使用真实数据来说明在疫苗接种正在进行中，提出了提出的方法来减轻这种传染的方法。

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