Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86281
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dc.contributor.authorOng, Janeten
dc.contributor.authorLiu, Xuen
dc.contributor.authorRajarethinam, Jayanthien
dc.contributor.authorKok, Suet Yhengen
dc.contributor.authorLiang, Shaohongen
dc.contributor.authorTang, Choon Siangen
dc.contributor.authorCook, Alex R.en
dc.contributor.authorNg, Lee Chingen
dc.contributor.authorYap, Graceen
dc.contributor.editorAlthouse, Benjaminen
dc.date.accessioned2018-07-25T08:02:49Zen
dc.date.accessioned2019-12-06T16:19:33Z-
dc.date.available2018-07-25T08:02:49Zen
dc.date.available2019-12-06T16:19:33Z-
dc.date.issued2018en
dc.identifier.citationOng, J., Liu, X., Rajarethinam, J., Kok, S. Y., Liang, S., Tang, C. S., et al. (2018). Mapping dengue risk in Singapore using Random Forest. PLOS Neglected Tropical Diseases, 12(6), e0006587-.en
dc.identifier.issn1935-2727en
dc.identifier.urihttps://hdl.handle.net/10356/86281-
dc.description.abstractBackground Singapore experiences endemic dengue, with 2013 being the largest outbreak year known to date, culminating in 22,170 cases. Given the limited resources available, and that vector control is the key approach for prevention in Singapore, it is important that public health professionals know where resources should be invested in. This study aims to stratify the spatial risk of dengue transmission in Singapore for effective deployment of resources. Methodology/principal findings Random Forest was used to predict the risk rank of dengue transmission in 1km2 grids, with dengue, population, entomological and environmental data. The predicted risk ranks are categorized and mapped to four color-coded risk groups for easy operation application. The risk maps were evaluated with dengue case and cluster data. Risk maps produced by Random Forest have high accuracy. More than 80% of the observed risk ranks fell within the 80% prediction interval. The observed and predicted risk ranks were highly correlated (≥0.86, P <0.01). Furthermore, the predicted risk levels were in excellent agreement with case density, a weighted Kappa coefficient of more than 0.80 (P <0.01). Close to 90% of the dengue clusters occur in high risk areas, and the odds of cluster forming in high risk areas were higher than in low risk areas. Conclusions This study demonstrates the potential of Random Forest and its strong predictive capability in stratifying the spatial risk of dengue transmission in Singapore. Dengue risk map produced using Random Forest has high accuracy, and is a good surveillance tool to guide vector control operations.en
dc.description.sponsorshipMOH (Min. of Health, S’pore)en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesPLOS Neglected Tropical Diseasesen
dc.rights© 2018 Ong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectDengueen
dc.subjectRandom Foresten
dc.titleMapping dengue risk in Singapore using Random Foresten
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen
dc.identifier.doi10.1371/journal.pntd.0006587en
dc.description.versionPublished versionen
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item.grantfulltextopen-
Appears in Collections:SBS Journal Articles
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