论文标题
通过统计学习中动态系统中的参数估计：重新解释近似于COVID-19的近似贝叶斯计算
Parameter estimation in dynamical systems via Statistical Learning: a reinterpretation of Approximate Bayesian Computation applied to COVID-19 spread
论文作者
论文摘要
我们为基于统计学习技术的动态系统提出了一种强大的参数估计方法，该方法旨在估算一组非常适合动力学的参数，以便获得有关其轨迹的定性行为的强大证据。该方法是相当通用和灵活的，因为它不依赖动态系统的任何特定属性，并且代表了通过统计学习的镜头重新解释近似贝叶斯计算方法。该方法对于估计流行病学隔室模型中的参数非常有用，以获得疾病进化的定性特性。我们将其应用于有关COVID-19在美国传播的模拟和真实数据，以便在定性地评估其随着时间的推移，以评估人们如何评估实施的措施的有效性，以减缓疾病当前和未来进化的差异和某些定性特征。
We propose a robust parameter estimation method for dynamical systems based on Statistical Learning techniques which aims to estimate a set of parameters that well fit the dynamics in order to obtain robust evidences about the qualitative behaviour of its trajectory. The method is quite general and flexible, since it does not rely on any specific property of the dynamical system, and represents a reinterpretation of Approximate Bayesian Computation methods through the lens of Statistical Learning. The method is specially useful for estimating parameters in epidemiological compartmental models in order to obtain qualitative properties of a disease evolution. We apply it to simulated and real data about COVID-19 spread in the US in order to evaluate qualitatively its evolution over time, showing how one may assess the effectiveness of measures implemented to slow the spread and some qualitative features of the disease current and future evolution.
