Data-Driven Framework for Uncovering Hidden Control Strategies in Evolutionary Analysis

TL;DR

This paper introduces a data-driven framework that uncovers hidden control strategies in evolutionary systems, exemplified by analyzing COVID-19 spread, extending control methods without prior system knowledge.

Contribution

The paper presents a novel algorithmic framework that estimates optimal control and evolutionary parameters simultaneously, applicable to complex dynamical systems like pandemics.

Findings

Successfully analyzed COVID-19 data in Japan and nine countries

Grouped countries based on shared epidemic response profiles

Demonstrated the framework's potential for understanding complex evolution processes

Abstract

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.