A Learning-Based Model Predictive Control Framework for Real-Time SIR Epidemic Mitigation

We propose a learning-based model predictive control framework for mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and consider testing for isolation as the control strategy. In the framework, we use a daily testing strategy to remove (isolate) a portion of the infected population. Our goal is to keep the daily infected population below a certain level, while minimizing the total number of tests. Distinct from existing works on leveraging model predictive control in epidemic spreading, we learn the model parameters and compute the feedback control signal simultaneously. We illustrate the results by numerical simulation using COVID-19 data from India.

The Impact of Vaccine Hesitancy on Epidemic Spreading

The COVID-19 pandemic has devastated the world in an unprecedented way, causing enormous loss of life. Time and again, public health authorities have urged people to become vaccinated to protect themselves and mitigate the spread of the disease. However, vaccine hesitancy has stalled vaccination levels in the United States. This study explores the effect of vaccine hesitancy on the spread of disease by introducing an SIRS-V, model, with compartments of susceptible (S), infected (I), recovered (R), and vaccinated (V). We leverage the concept of carrying capacity to account for vaccine hesitancy by defining a vaccine confidence level n, which is the maximum number of people that will become vaccinated during the course of a disease. The inverse of vaccine confidence is vaccine hesitance, W. We explore the equilibria of the SIRSV, model and their stability, and illustrate the impact of vaccine hesitance on epidemic spread analytically and via simulations.

Overcoming Challenges for Estimating Virus Spread Dynamics from Data

In this paper we investigate estimating the parameters of a discrete time networked virus spread model from time series data. We explore the effect of multiple challenges on the estimation process including system noise, missing data, time-varying network structure, and quantization of the measurements. We also demonstrate how well a heterogeneous model can be captured by homogeneous model parameters. We further illustrate these challenges by employing recent data collected from the ongoing 2019 novel coronavirus (2019-nCoV) outbreak, motivating future work.

Capturing the Effects of Transportation on the Spread of COVID-19 with a Multi-Networked SEIR Model

In this paper we present a deterministic discrete-time networked SEIR model that includes a number of transportation networks, and present assumptions under which it is well defined. We analyze the limiting behavior of the model and present necessary and sufficient conditions for estimating the spreading parameters from data. We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.