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. Pare

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
JournalIEEE Control Systems Letters
DOIs
StateAccepted/In press - 2021

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Analytical models
  • COVID-19
  • COVID-19.
  • Control applications
  • Data models
  • Limiting
  • Pandemics
  • SEIR model
  • Transportation
  • Viruses (medical)
  • transportation networks

Fingerprint

Dive into the research topics of 'Capturing the Effects of Transportation on the Spread of COVID-19 with a Multi-Networked SEIR Model'. Together they form a unique fingerprint.

Cite this