Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105757
Title: Using human movement data to identify potential areas of Zika transmission : case study of the largest Zika cluster in Singapore
Authors: Rajarethinam, Jayanthi
Ong, Janet
Lim, Shi-Hui
Tay, Yu-Heng
Bounliphone, Wacha
Chong, Chee-Seng
Yap, Grace
Ng, Lee-Ching
Keywords: Zika
DRNTU::Science::Biological sciences
Human Movement
Issue Date: 2019
Source: 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
Series/Report no.: International Journal of Environmental Research and Public Health
Abstract: 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.
URI: https://hdl.handle.net/10356/105757
http://hdl.handle.net/10220/48745
ISSN: 1661-7827
DOI: 10.3390/ijerph16050808
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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