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

Civil, Environmental, and Geo- Engineering

Research output: Contribution to journal › Article › peer-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 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	Accepted/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

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10.1109/LCSYS.2021.3050954

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

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.",

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

author = "Damir Vrabac and Mingfeng Shang and Brooks Butler and Joseph Pham and Raphael Stern and Pare, {Philip E.}",

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. Par{\'e}.) Damir Vrabac is with the Department of Computer Science, Stanford University, 13137 Nacka, Sweden (e-mail: dvrabac@stanford.edu). Publisher Copyright: {\textcopyright} 2017 IEEE.",

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doi = "10.1109/LCSYS.2021.3050954",

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AU - Pham, Joseph

AU - Stern, Raphael

AU - Pare, Philip E.

N1 - 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.

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AB - 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.

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KW - Viruses (medical)

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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

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