Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission

In this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alon...

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Main Authors: Sung Jin Lee, Sang Eun Lee, Ji-On Kim, Gi Bum Kim
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Informatics
Subjects:
Online Access:https://www.mdpi.com/2227-9709/8/2/22
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spelling doaj-abbed1b9b8af40e5981c230a6cbd962b2021-03-26T00:01:22ZengMDPI AGInformatics2227-97092021-03-018222210.3390/informatics8020022Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community TransmissionSung Jin Lee0Sang Eun Lee1Ji-On Kim2Gi Bum Kim3Department of Forensics, Sungkyunkwan University, Seoul 03063, KoreaDepartment of Forensics, Sungkyunkwan University, Seoul 03063, KoreaDepartment of Police Science, Korea National Police University, Asan 10050, KoreaDepartment of Forensics, Sungkyunkwan University, Seoul 03063, KoreaIn this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alone. Consequently, we study a method that the authority predicts the transmission route of COVID-19 between visitors in the community from a spatiotemporal perspective. This method models a contact network around the first confirmed case, allowing the health authorities to conduct tests on visitors after an outbreak of COVID-19 in the community. After securing the GPS data of community visitors, it traces back to the past from the time the first confirmed case occurred and creates contact clusters at each time step. This is different from other researches that focus on identifying the movement paths of confirmed patients by forward tracing. The proposed method creates the contact network by assigning weights to each contact cluster based on the degree of proximity between contacts. Identifying the source of infection in the contact network can make us predict the transmission route between the first confirmed case and the source of infection and classify the contacts on the transmission route. In this experiment, we used 64,073 simulated data for 100 people and extracted the transmission route and a top 10 list for centrality analysis. The contacts on the route path can be quickly designated as a priority for COVID-19 testing. In addition, it is possible for the authority to extract the subjects with high influence from the centrality theory and use them for additional COVID-19 epidemic investigation that requires urgency. This model is expected to be used in the epidemic investigation requiring the quick selection of close contacts.https://www.mdpi.com/2227-9709/8/2/22COVID-19contact networkroute of transmissionsocial network analysiscentralityclustering
collection DOAJ
language English
format Article
sources DOAJ
author Sung Jin Lee
Sang Eun Lee
Ji-On Kim
Gi Bum Kim
spellingShingle Sung Jin Lee
Sang Eun Lee
Ji-On Kim
Gi Bum Kim
Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
Informatics
COVID-19
contact network
route of transmission
social network analysis
centrality
clustering
author_facet Sung Jin Lee
Sang Eun Lee
Ji-On Kim
Gi Bum Kim
author_sort Sung Jin Lee
title Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
title_short Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
title_full Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
title_fullStr Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
title_full_unstemmed Two-Way Contact Network Modeling for Identifying the Route of COVID-19 Community Transmission
title_sort two-way contact network modeling for identifying the route of covid-19 community transmission
publisher MDPI AG
series Informatics
issn 2227-9709
publishDate 2021-03-01
description In this study, we address the problem originated from the fact that “The Corona 19 Epidemiological Research Support System,” developed by the Korea Centers for Disease Control and Prevention, is limited to analyzing the Global Positioning System (GPS) information of the confirmed COVID-19 cases alone. Consequently, we study a method that the authority predicts the transmission route of COVID-19 between visitors in the community from a spatiotemporal perspective. This method models a contact network around the first confirmed case, allowing the health authorities to conduct tests on visitors after an outbreak of COVID-19 in the community. After securing the GPS data of community visitors, it traces back to the past from the time the first confirmed case occurred and creates contact clusters at each time step. This is different from other researches that focus on identifying the movement paths of confirmed patients by forward tracing. The proposed method creates the contact network by assigning weights to each contact cluster based on the degree of proximity between contacts. Identifying the source of infection in the contact network can make us predict the transmission route between the first confirmed case and the source of infection and classify the contacts on the transmission route. In this experiment, we used 64,073 simulated data for 100 people and extracted the transmission route and a top 10 list for centrality analysis. The contacts on the route path can be quickly designated as a priority for COVID-19 testing. In addition, it is possible for the authority to extract the subjects with high influence from the centrality theory and use them for additional COVID-19 epidemic investigation that requires urgency. This model is expected to be used in the epidemic investigation requiring the quick selection of close contacts.
topic COVID-19
contact network
route of transmission
social network analysis
centrality
clustering
url https://www.mdpi.com/2227-9709/8/2/22
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