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 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.

Original languageEnglish (US)
Article number9319714
Pages (from-to)103-108
Number of pages6
JournalIEEE Control Systems Letters
Volume6
DOIs
StateAccepted/In press - 2021

Bibliographical note

Funding Information:
Manuscript received September 14, 2020; revised December 8, 2020; accepted December 28, 2020. Date of publication January 11, 2021; date of current version June 23, 2021. This work was supported by the National Science Foundation under Grant CNS-2028946 (R.S.) and Grant CNS-2028738 (P.E.P.). Recommended by Senior Editor M. Arcak. (Corresponding author: Philip E. Paré.) Damir Vrabac is with the Department of Computer Science, Stanford University, 13137 Nacka, Sweden (e-mail: dvrabac@stanford.edu).

Publisher Copyright:
© 2017 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