Environmental Health Institute, National Environment Agency, Singapore.
Environmental Public Health Operations, National Environment Agency, Singapore.
PLoS Negl Trop Dis. 2018 Jun 18;12(6):e0006587. doi: 10.1371/journal.pntd.0006587. eCollection 2018 Jun.
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.
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.
新加坡存在登革热本地传播，2013 年是已知的最大暴发年份，累计病例达 22170 例。鉴于可用资源有限，而在新加坡，病媒控制是预防的关键方法，了解资源应投资于何处对公共卫生专业人员而言非常重要。本研究旨在对新加坡登革热传播的空间风险进行分层，以便有效部署资源。
方法/主要发现：使用随机森林来预测以 1km2 网格为单位的登革热传播风险等级，输入登革热、人口、昆虫学和环境数据。将预测的风险等级分类并映射到四个颜色编码的风险组，以便于操作应用。使用登革热病例和集群数据对风险图进行评估。随机森林生成的风险图具有很高的准确性。超过 80%的观察到的风险等级落在 80%的预测区间内。观察到的和预测的风险等级高度相关([Formula: see text]≥0.86，P<0.01)。此外，预测的风险水平与病例密度非常吻合，加权 Kappa 系数超过 0.80(P<0.01)。接近 90%的登革热集群发生在高风险地区，集群在高风险地区形成的可能性高于低风险地区。
本研究证明了随机森林在分层新加坡登革热传播空间风险方面的潜力及其强大的预测能力。使用随机森林生成的登革热风险图具有很高的准确性，是指导病媒控制行动的良好监测工具。
