Mapping dengue risk in Singapore using Random Forest.

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 ([Formula: see text]≥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.

journal_name

PLoS Negl Trop Dis

authors

Ong J,Liu X,Rajarethinam J,Kok SY,Liang S,Tang CS,Cook AR,Ng LC,Yap G

doi

10.1371/journal.pntd.0006587

subject

Has Abstract

pub_date

2018-06-18 00:00:00

pages

e0006587

issue

6

eissn

1935-2727

issn

1935-2735

pii

PNTD-D-17-01640

journal_volume

12

pub_type

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