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.