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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427696/ https://www.ncbi.nlm.nih.gov/pubmed/30841598 http://dx.doi.org/10.3390/ijerph16050808 |
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author | Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching |
author_facet | Rajarethinam, Jayanthi Ong, Janet Lim, Shi-Hui Tay, Yu-Heng Bounliphone, Wacha Chong, Chee-Seng Yap, Grace Ng, Lee-Ching |
author_sort | Rajarethinam, Jayanthi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6427696 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64276962019-04-10 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 Int J Environ Res Public Health Article 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. MDPI 2019-03-05 2019-03 /pmc/articles/PMC6427696/ /pubmed/30841598 http://dx.doi.org/10.3390/ijerph16050808 Text en © 2019 by the authors. 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/). |
spellingShingle | Article 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 |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427696/ https://www.ncbi.nlm.nih.gov/pubmed/30841598 http://dx.doi.org/10.3390/ijerph16050808 |
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