How does a Rational Agent Act in an Epidemic?

The evolution of a disease in a large population is a function of the top-down policy measures from a centralized planner and the self-interested decisions (to be socially active) of individual agents in a large heterogeneous population. This paper is concerned with understanding the latter based on a mean-field type optimal control model. Specifically, the model is used to investigate the role of partial information on an agent's decision-making and study the impact of such decisions by a large number of agents on the spread of the virus in the population. The motivation comes from the presymptomatic and asymptomatic spread of the COVID-19 virus, where an agent unwittingly spreads the virus. We show that even in a setting with fully rational agents, limited information on the viral state can result in epidemic growth. © 2022 IEEE.

A Data-Informed Approach for Analysis, Validation, and Identification of COVID-19 Models

The COVID-19 pandemic has generated an enormous amount of data, providing a unique opportunity for modeling and analysis. In this paper, we present a data-informed approach for building stochastic compartmental models that is grounded in the Markovian processes underlying these models. Our initial data analyses reveal that the SIRD model - susceptiple (S), infected (I), recovered (R), and death (D) - is not consistent with the data. In particular, the transition times expressed in the dataset do not obey exponential distributions, implying that there exist unmodeled (hidden) states. We make use of the available epidemiological data to inform the location of these hidden states, allowing us to develop an augmented compartmental model which includes states for hospitalization (H) and end of infectious viral shedding (V). Using the proposed model, we characterize delay distributions analytically and match model parameters to empirical quantities in the data to obtain a good model fit. Insights from an epidemiological perspective are presented, as well as their implications for mitigation and control strategies.

Optimal Transportation Methods in Nonlinear Filtering

How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily deaths, the number of daily hospitalizations, and the number of daily positive tests). The underlying mathematics for posing and solving this and several other partially observed dynamic problems is familiar to control theorists © 1991-2012 IEEE.