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|Title:||Mapping dengue risk in Singapore using Random Forest||Authors:||Ong, Janet
Kok, Suet Yheng
Tang, Choon Siang
Cook, Alex R.
Ng, Lee Ching
|Issue Date:||2018||Source:||Ong, 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-.||Series/Report no.:||PLOS Neglected Tropical Diseases||Abstract:||Background 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.||URI:||https://hdl.handle.net/10356/86281
|ISSN:||1935-2727||DOI:||http://dx.doi.org/10.1371/journal.pntd.0006587||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.||metadata.item.grantfulltext:||open||metadata.item.fulltext:||With Fulltext|
|Appears in Collections:||SBS Journal Articles|
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