Reproduction Factor Based Latent Epidemic Model Inference: A Data-Driven Approach Using COVID-19 Datasets.

The mathematical modeling of infectious diseases aims to evaluate the transmissibility of the on-going spread of disease and guide the government's control strategies and interventions. In this paper, we propose a novel transmissibility indicator, reproduction factor, which evaluates the number of secondary infections from a single nonisolated infectious individual. In contrast to classic reproduction numbers, the reproduction factor explicitly considers the fraction of susceptible individuals (who are not immune to disease naturally or through vaccination) and the nonisolated population to evaluate near real-time transmissibility.

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics.

A fundamental question in neuroscience is how the brain creates an internal model of the world to guide actions using sequences of ambiguous sensory information. This is naturally formulated as a reinforcement learning problem under partial observations, where an agent must estimate relevant latent variables in the world from its evidence, anticipate possible future states, and choose actions that optimize total expected reward. This problem can be solved by control theory, which allows us to find the optimal actions for a given system dynamics and objective function.

Rational thoughts in neural codes.

Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning to reach subjective goals. A fundamental challenge in neuroscience is, How can we use behavior and neural activity to understand this internal model and its dynamic latent variables? Here we interpret behavioral data by assuming an agent behaves rationally—that is, it takes actions that optimize its subjective reward according to its understanding of the task and its relevant causal variables. We apply a method, inverse rational control (IRC), to learn an agent’s internal model and reward function by maximizing the likelihood of its measured sensory observations and actions.