We have devised a data-driven framework for uncovering hidden control strategies used by an evolutionary system described by an evolutionary probability distribution. This innovative framework enables deciphering of the concealed mechanisms that contribute to the progression or mitigation of such situations as the spread of COVID-19. Novel algorithms are used to estimate the optimal control in tandem with the parameters for evolution in general dynamical systems, thereby extending the concept of model predictive control. This is a significant departure from conventional control methods, which require knowledge of the system to manipulate its evolution and of the controller's strategy or parameters. We used a generalized additive model, supplemented by extensive statistical testing, to identify a set of predictor covariates closely linked to the control. Using real-world COVID-19 data, we successfully delineated the descriptive behaviors of the COVID-19 epidemics in five prefectures in Japan and nine countries. We compared these nine countries and grouped them on the basis of shared profiles, providing valuable insights into their pandemic responses. Our findings underscore the potential of our framework as a powerful tool for understanding and managing complex evolutionary processes.

翻译：我们提出了一种数据驱动框架，用于揭示由演化概率分布描述的演化系统所采用的隐藏控制策略。这一创新框架能够破译促成或缓解COVID-19传播等态势演变的隐藏机制。通过新型算法，我们实现了对一般动力系统中演化参数与最优控制的联合估计，从而拓展了模型预测控制的概念。这与传统控制方法存在本质区别——传统方法要求预先掌握系统的演化操控知识以及控制器的策略或参数。我们采用广义加性模型，辅以大规模统计检验，识别出与控制器紧密关联的一组预测协变量。基于真实COVID-19数据，我们成功描绘了日本五个县及九个国家的COVID-19疫情表征行为，并通过聚类分析对比这些国家，依据其共有特征进行分组，为各国疫情应对策略提供了重要见解。研究结果验证了该框架作为理解和驾驭复杂演化过程有力工具的潜力。