Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore
Singapore experienced its first Zika virus (ZIKV) cluster in August 2016. To understand the implication of human movement on disease spread, a retrospective study was conducted using aggregated and anonymized mobile phone data to examine movement from the cluster to identify areas of possible transm...
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sg-ntu-dr.10356-1057572023-02-28T17:06:29Z Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching School of Biological Sciences Zika DRNTU::Science::Biological sciences Human Movement Singapore experienced its first Zika virus (ZIKV) cluster in August 2016. To understand the implication of human movement on disease spread, a retrospective study was conducted using aggregated and anonymized mobile phone data to examine movement from the cluster to identify areas of possible transmission. An origin–destination model was developed based on the movement of three groups of individuals: (i) construction workers, (ii) residents and (iii) visitors out of the cluster locality to other parts of the island. The odds ratio of ZIKV cases in a hexagon visited by an individual from the cluster, independent of the group of individuals, is 3.20 (95% CI: 2.65–3.87, p-value < 0.05), reflecting a higher count of ZIKV cases when there is a movement into a hexagon from the cluster locality. A comparison of independent ROC curves tested the statistical significance of the difference between the areas under the curves of the three groups of individuals. Visitors (difference in AUC = 0.119) and residents (difference in AUC = 0.124) have a significantly larger difference in area under the curve compared to the construction workers (p-value < 0.05). This study supports the proof of concept of using mobile phone data to approximate population movement, thus identifying areas at risk of disease transmission. Published version 2019-06-14T02:12:33Z 2019-12-06T21:57:22Z 2019-06-14T02:12:33Z 2019-12-06T21:57:22Z 2019 Journal Article Rajarethinam, J., Ong, J., Lim, S.-H., Tay, Y.-H., Bounliphone, W., Chong, C.-S., . . . Ng, L.-C. (2019). Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore. International Journal of Environmental Research and Public Health, 16(5), 808-. doi:10.3390/ijerph16050808 1661-7827 https://hdl.handle.net/10356/105757 http://hdl.handle.net/10220/48745 10.3390/ijerph16050808 en International Journal of Environmental Research and Public Health © 2019 The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 13 p. application/pdf |
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Zika DRNTU::Science::Biological sciences Human Movement Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
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Singapore experienced its first Zika virus (ZIKV) cluster in August 2016. To understand the implication of human movement on disease spread, a retrospective study was conducted using aggregated and anonymized mobile phone data to examine movement from the cluster to identify areas of possible transmission. An origin–destination model was developed based on the movement of three groups of individuals: (i) construction workers, (ii) residents and (iii) visitors out of the cluster locality to other parts of the island. The odds ratio of ZIKV cases in a hexagon visited by an individual from the cluster, independent of the group of individuals, is 3.20 (95% CI: 2.65–3.87, p-value < 0.05), reflecting a higher count of ZIKV cases when there is a movement into a hexagon from the cluster locality. A comparison of independent ROC curves tested the statistical significance of the difference between the areas under the curves of the three groups of individuals. Visitors (difference in AUC = 0.119) and residents (difference in AUC = 0.124) have a significantly larger difference in area under the curve compared to the construction workers (p-value < 0.05). This study supports the proof of concept of using mobile phone data to approximate population movement, thus identifying areas at risk of disease transmission. |
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School of Biological Sciences |
author_facet |
School of Biological Sciences Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching |
format |
Article |
author |
Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching |
author_sort |
Rajarethinam, Jayanthi |
title |
Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
title_short |
Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
title_full |
Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
title_fullStr |
Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
title_full_unstemmed |
Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore |
title_sort |
using human movement data to identify potential areas of zika transmission : case study of the largest zika cluster in singapore |
publishDate |
2019 |
url |
https://hdl.handle.net/10356/105757 http://hdl.handle.net/10220/48745 |
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1759858365005037568 |