Capturing the Effects of Transportation on the Spread of COVID-19 With a Multi-Networked SEIR Model
Damir Vrabac, Mingfeng Shang, Brooks Butler, Joseph Pham, Raphael Stern, Philip E. Paré
Abstract
In this letter 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.
Topics & Concepts
LimitingCoronavirus disease 2019 (COVID-19)Computer scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakEconometricsEngineeringMathematicsVirologyInfectious disease (medical specialty)MedicineOutbreakPathologyMechanical engineeringBiologyDiseaseCOVID-19 epidemiological studiesData-Driven Disease SurveillanceSARS-CoV-2 and COVID-19 Research