Modeling Dynamics of Covid-19 Infected Population with PSO

Guangdong Huang; Aihua Li

doi:10.1007/978-981-16-7913-1_6

Modeling Dynamics of Covid-19 Infected Population with PSO

Guangdong Huang, Aihua Li

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This study focuses on the Covid-19 spreading dynamics in Bergen County, New Jersey, USA. Due to limited covid-19 testing capacity, it was difficult to assess the real data about the virus spreading in New Jersey counties. Our study is based on the available incomplete daily data from March 15 to July 15 of 2020. In order to capture an overall picture of the local dynamics of the infected population and predict reasonable future situations, we perform several traditional dynamic modeling methods. A region-stage-modified-SEIR model (denoted MSEIR) and a SEIRH model are constructed to describe the dynamics of the infected population. Particle Swarm Optimization (PSO) is used to identify the parameters of the developed models. In order to predict the cumulative number of the infected individuals, the produced models are used to simulate the dynamics of the population in four epidemiological groups respectively: susceptible, exposed, infected, and recovered groups. By this process, we obtain a better picture of the COVID-19 infected individuals in the target county.

Original language	English
Title of host publication	Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings
Editors	Limei Lin, Yuhong Liu, Chia-Wei Lee
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	75-89
Number of pages	15
ISBN (Print)	9789811679124
DOIs	https://doi.org/10.1007/978-981-16-7913-1_6
State	Published - 2021
Event	7th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2021 - Fuzhou, China Duration: 19 Nov 2021 → 21 Nov 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1495 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	7th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2021
Country/Territory	China
City	Fuzhou
Period	19/11/21 → 21/11/21

Keywords

COVID-19
MSEIR
PSO
SEIRH

Access to Document

10.1007/978-981-16-7913-1_6

Cite this

Huang, G., & Li, A. (2021). Modeling Dynamics of Covid-19 Infected Population with PSO. In L. Lin, Y. Liu, & C-W. Lee (Eds.), Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings (pp. 75-89). (Communications in Computer and Information Science; Vol. 1495 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-7913-1_6

Huang, Guangdong ; Li, Aihua. / Modeling Dynamics of Covid-19 Infected Population with PSO. Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings. editor / Limei Lin ; Yuhong Liu ; Chia-Wei Lee. Springer Science and Business Media Deutschland GmbH, 2021. pp. 75-89 (Communications in Computer and Information Science).

@inproceedings{fa55524025454f8e8f8cc1001f5245a7,

title = "Modeling Dynamics of Covid-19 Infected Population with PSO",

abstract = "This study focuses on the Covid-19 spreading dynamics in Bergen County, New Jersey, USA. Due to limited covid-19 testing capacity, it was difficult to assess the real data about the virus spreading in New Jersey counties. Our study is based on the available incomplete daily data from March 15 to July 15 of 2020. In order to capture an overall picture of the local dynamics of the infected population and predict reasonable future situations, we perform several traditional dynamic modeling methods. A region-stage-modified-SEIR model (denoted MSEIR) and a SEIRH model are constructed to describe the dynamics of the infected population. Particle Swarm Optimization (PSO) is used to identify the parameters of the developed models. In order to predict the cumulative number of the infected individuals, the produced models are used to simulate the dynamics of the population in four epidemiological groups respectively: susceptible, exposed, infected, and recovered groups. By this process, we obtain a better picture of the COVID-19 infected individuals in the target county.",

keywords = "COVID-19, MSEIR, PSO, SEIRH",

author = "Guangdong Huang and Aihua Li",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Singapore Pte Ltd.; null ; Conference date: 19-11-2021 Through 21-11-2021",

year = "2021",

doi = "10.1007/978-981-16-7913-1_6",

language = "English",

isbn = "9789811679124",

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Huang, G & Li, A 2021, Modeling Dynamics of Covid-19 Infected Population with PSO. in L Lin, Y Liu & C-W Lee (eds), Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings. Communications in Computer and Information Science, vol. 1495 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 75-89, 7th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2021, Fuzhou, China, 19/11/21. https://doi.org/10.1007/978-981-16-7913-1_6

Modeling Dynamics of Covid-19 Infected Population with PSO. / Huang, Guangdong; Li, Aihua.

Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings. ed. / Limei Lin; Yuhong Liu; Chia-Wei Lee. Springer Science and Business Media Deutschland GmbH, 2021. p. 75-89 (Communications in Computer and Information Science; Vol. 1495 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modeling Dynamics of Covid-19 Infected Population with PSO

AU - Huang, Guangdong

AU - Li, Aihua

PY - 2021

Y1 - 2021

N2 - This study focuses on the Covid-19 spreading dynamics in Bergen County, New Jersey, USA. Due to limited covid-19 testing capacity, it was difficult to assess the real data about the virus spreading in New Jersey counties. Our study is based on the available incomplete daily data from March 15 to July 15 of 2020. In order to capture an overall picture of the local dynamics of the infected population and predict reasonable future situations, we perform several traditional dynamic modeling methods. A region-stage-modified-SEIR model (denoted MSEIR) and a SEIRH model are constructed to describe the dynamics of the infected population. Particle Swarm Optimization (PSO) is used to identify the parameters of the developed models. In order to predict the cumulative number of the infected individuals, the produced models are used to simulate the dynamics of the population in four epidemiological groups respectively: susceptible, exposed, infected, and recovered groups. By this process, we obtain a better picture of the COVID-19 infected individuals in the target county.

AB - This study focuses on the Covid-19 spreading dynamics in Bergen County, New Jersey, USA. Due to limited covid-19 testing capacity, it was difficult to assess the real data about the virus spreading in New Jersey counties. Our study is based on the available incomplete daily data from March 15 to July 15 of 2020. In order to capture an overall picture of the local dynamics of the infected population and predict reasonable future situations, we perform several traditional dynamic modeling methods. A region-stage-modified-SEIR model (denoted MSEIR) and a SEIRH model are constructed to describe the dynamics of the infected population. Particle Swarm Optimization (PSO) is used to identify the parameters of the developed models. In order to predict the cumulative number of the infected individuals, the produced models are used to simulate the dynamics of the population in four epidemiological groups respectively: susceptible, exposed, infected, and recovered groups. By this process, we obtain a better picture of the COVID-19 infected individuals in the target county.

KW - COVID-19

KW - MSEIR

KW - PSO

KW - SEIRH

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U2 - 10.1007/978-981-16-7913-1_6

DO - 10.1007/978-981-16-7913-1_6

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SN - 9789811679124

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BT - Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings

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A2 - Lee, Chia-Wei

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 19 November 2021 through 21 November 2021

ER -

Huang G, Li A. Modeling Dynamics of Covid-19 Infected Population with PSO. In Lin L, Liu Y, Lee C-W, editors, Security and Privacy in Social Networks and Big Data - 7th International Symposium, SocialSec 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 75-89. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-16-7913-1_6

Modeling Dynamics of Covid-19 Infected Population with PSO

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Keywords

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