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: Chapter in Book/Report/Conference proceedingConference contribution

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)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3152-3157
Number of pages6
ISBN (Electronic)9781665441971
DOIs
StatePublished - May 25 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: May 25 2021May 28 2021

Publication series

NameProceedings of the American Control Conference
Volume2021-May
ISSN (Print)0743-1619

Conference

Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Period5/25/215/28/21

Bibliographical note

Funding Information:
This manuscript was first submitted for review on September 14, 2020. This material is based upon work supported by the National Science Foundation under Grant No. CNS-2028946 (R.S.) and Grant No. CNS-2028738 (P.E.P.).

Publisher Copyright:
© 2021 American Automatic Control Council.

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