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

24 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

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: [email protected]).

Publisher Copyright:
© 2017 IEEE.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

COVID-19
Control applications
SEIR model
transportation networks

Publisher link

10.1109/LCSYS.2021.3050954

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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 = "COVID-19, Control applications, SEIR model, transportation networks",

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

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doi = "10.1109/LCSYS.2021.3050954",

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volume = "6",

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AU - Butler, Brooks

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: [email protected]). Publisher Copyright: © 2017 IEEE.

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N2 - 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.

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 - Control applications

KW - SEIR model

KW - transportation networks

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

Abstract

Bibliographical note

UN SDGs

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