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

doi:10.1109/LCSYS.2021.3050954

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 journal › Article › peer-review

18 Scopus citations

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 language	English (US)
Article number	9319714
Pages (from-to)	103-108
Number of pages	6
Journal	IEEE Control Systems Letters
Volume	6
DOIs	https://doi.org/10.1109/LCSYS.2021.3050954
State	Published - 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

COVID-19
Control applications
SEIR model
transportation networks

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/LCSYS.2021.3050954

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AU - Stern, Raphael

AU - Pare, Philip E.

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Capturing the Effects of Transportation on the Spread of COVID-19 with a Multi-Networked SEIR Model

Abstract

Bibliographical note

Keywords

UN SDGs

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