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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: University of Malaysia Sarawak 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
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spelling my.unimas.ir.416792023-04-18T02:18:13Z http://ir.unimas.my/id/eprint/41679/ Identifying COVID-19 Hotspots using Bipartite Network Approach 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 2023-03-17 Thesis NonPeerReviewed text en http://ir.unimas.my/id/eprint/41679/5/MasterSci.%20Thesis_Hong%20Boon%20Hao%20-%20%2024%20pages.pdf text en http://ir.unimas.my/id/eprint/41679/6/MasterSci.%20Thesis_Hong%20Boon%20Hao_fulltext.pdf text en http://ir.unimas.my/id/eprint/41679/8/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%202.pdf text en http://ir.unimas.my/id/eprint/41679/9/MSc.%20Thesis_Hong%20Boon%20Hao_%20supervisor%20approval%20form%201.pdf Hong, Boon Hao (2023) Identifying COVID-19 Hotspots using Bipartite Network Approach. Masters thesis, University of Malaysia Sarawak.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
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
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
publisher University of Malaysia Sarawak
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
http://ir.unimas.my/id/eprint/41679/
_version_ 1765301195262394368
score 13.211869

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