  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.
