Identifying COVID-19 Hotspots using Bipartite Network Approach

The COVID-19 pandemic has affected countries worldwide, causing major disruptions in both health and economic systems. While lockdowns have been effective in controlling the spread of the virus, their negative impact on the economy has prompted the need for alternative, cost-balanced control measure...

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Main Author:	Hong, Boon Hao
Format:	Thesis
Language:	English English English English
Published:	2023
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/41679/5/MasterSci.%20Thesis_Hong%20Boon%20Hao%20-%20%2024%20pages.pdf http://ir.unimas.my/id/eprint/41679/6/MasterSci.%20Thesis_Hong%20Boon%20Hao_fulltext.pdf http://ir.unimas.my/id/eprint/41679/8/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%202.pdf http://ir.unimas.my/id/eprint/41679/9/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%201.pdf
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spelling	my-unimas-ir.416792023-04-18T02:18:13Z Identifying COVID-19 Hotspots using Bipartite Network Approach 2023-03-17 Hong, Boon Hao QA75 Electronic computers. Computer science The COVID-19 pandemic has affected countries worldwide, causing major disruptions in both health and economic systems. While lockdowns have been effective in controlling the spread of the virus, their negative impact on the economy has prompted the need for alternative, cost-balanced control measures. Contact tracing has emerged as a promising solution in identifying community outbreaks of COVID-19. To improve the efficacy of contact tracing, this study aimed to formulate a contact network model for COVID-19 transmission. Conventional approaches were found to be inadequate in modelling the transmission of COVID-19, particularly in identifying the source of infection. To address this, the study utilized a bipartite network modelling approach to account for the heterogeneity of transmission routes, human hosts, and visited locations. The human host and visited location were identified as the two discrete entities in the research scenario. Using data from the Bintulu Health Office's contact tracing investigation forms, six network models were formulated. The link weight between the human host and location nodes was quantified using the summation rule, taking into consideration various factors such as environmental properties, building characteristics, human and pathogen characteristics, and transmission modes. The location and human nodes were then ranked using a web-based search algorithm based on their respective ranking values. The results of the study showed that the bipartite network modelling approach was successful in formulating the contact network model. Verification analysis revealed a root mean square error of 0.0002244 and 0.001419 for the location and human nodes, respectively, which were well within the threshold value of 0.05. The ranking between the target and validated models was found to have strong similarity with a good Spearman’s rank correlation coefficient of above 0.70 (p < 0.001), indicating a high degree of relevance in improving contact tracing for COVID-19. The study also found that all parameters used in the model were relatively significant, and that the model had the ability to predict potential hotspots with 90% accuracy within a 600m radius for the subsequent week. These findings highlight the potential of the bipartite network modeling approach in improving contact tracing for COVID-19 and reducing the spread of the virus in high-risk areas. University of Malaysia Sarawak 2023-03 Thesis http://ir.unimas.my/id/eprint/41679/ http://ir.unimas.my/id/eprint/41679/5/MasterSci.%20Thesis_Hong%20Boon%20Hao%20-%20%2024%20pages.pdf text en public http://ir.unimas.my/id/eprint/41679/6/MasterSci.%20Thesis_Hong%20Boon%20Hao_fulltext.pdf text en validuser http://ir.unimas.my/id/eprint/41679/8/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%202.pdf text en staffonly http://ir.unimas.my/id/eprint/41679/9/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%201.pdf text en staffonly masters University of Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English English English English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Hong, Boon Hao Identifying COVID-19 Hotspots using Bipartite Network Approach
description	The COVID-19 pandemic has affected countries worldwide, causing major disruptions in both health and economic systems. While lockdowns have been effective in controlling the spread of the virus, their negative impact on the economy has prompted the need for alternative, cost-balanced control measures. Contact tracing has emerged as a promising solution in identifying community outbreaks of COVID-19. To improve the efficacy of contact tracing, this study aimed to formulate a contact network model for COVID-19 transmission. Conventional approaches were found to be inadequate in modelling the transmission of COVID-19, particularly in identifying the source of infection. To address this, the study utilized a bipartite network modelling approach to account for the heterogeneity of transmission routes, human hosts, and visited locations. The human host and visited location were identified as the two discrete entities in the research scenario. Using data from the Bintulu Health Office's contact tracing investigation forms, six network models were formulated. The link weight between the human host and location nodes was quantified using the summation rule, taking into consideration various factors such as environmental properties, building characteristics, human and pathogen characteristics, and transmission modes. The location and human nodes were then ranked using a web-based search algorithm based on their respective ranking values. The results of the study showed that the bipartite network modelling approach was successful in formulating the contact network model. Verification analysis revealed a root mean square error of 0.0002244 and 0.001419 for the location and human nodes, respectively, which were well within the threshold value of 0.05. The ranking between the target and validated models was found to have strong similarity with a good Spearman’s rank correlation coefficient of above 0.70 (p < 0.001), indicating a high degree of relevance in improving contact tracing for COVID-19. The study also found that all parameters used in the model were relatively significant, and that the model had the ability to predict potential hotspots with 90% accuracy within a 600m radius for the subsequent week. These findings highlight the potential of the bipartite network modeling approach in improving contact tracing for COVID-19 and reducing the spread of the virus in high-risk areas.
format	Thesis
qualification_level	Master's degree
author	Hong, Boon Hao
author_facet	Hong, Boon Hao
author_sort	Hong, Boon Hao
title	Identifying COVID-19 Hotspots using Bipartite Network Approach
title_short	Identifying COVID-19 Hotspots using Bipartite Network Approach
title_full	Identifying COVID-19 Hotspots using Bipartite Network Approach
title_fullStr	Identifying COVID-19 Hotspots using Bipartite Network Approach
title_full_unstemmed	Identifying COVID-19 Hotspots using Bipartite Network Approach
title_sort	identifying covid-19 hotspots using bipartite network approach
granting_institution	University of Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2023
url	http://ir.unimas.my/id/eprint/41679/5/MasterSci.%20Thesis_Hong%20Boon%20Hao%20-%20%2024%20pages.pdf http://ir.unimas.my/id/eprint/41679/6/MasterSci.%20Thesis_Hong%20Boon%20Hao_fulltext.pdf http://ir.unimas.my/id/eprint/41679/8/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%202.pdf http://ir.unimas.my/id/eprint/41679/9/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%201.pdf
_version_	1783728527328149504

Identifying COVID-19 Hotspots using Bipartite Network Approach

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