Mapping dengue risk in Singapore using Random Forest.
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 reso...
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ftdoajarticles:oai:doaj.org/article:b224102494134548a94e965fb87e9b4d 2023-05-15T15:10:43+02:00 Mapping dengue risk in Singapore using Random Forest. Janet Ong Xu Liu Jayanthi Rajarethinam Suet Yheng Kok Shaohong Liang Choon Siang Tang Alex R Cook Lee Ching Ng Grace Yap 2018-06-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0006587 https://doaj.org/article/b224102494134548a94e965fb87e9b4d EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC6023234?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006587 https://doaj.org/article/b224102494134548a94e965fb87e9b4d PLoS Neglected Tropical Diseases, Vol 12, Iss 6, p e0006587 (2018) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2018 ftdoajarticles https://doi.org/10.1371/journal.pntd.0006587 2022-12-31T13:11:44Z 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.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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 12 6 e0006587 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Janet Ong Xu Liu Jayanthi Rajarethinam Suet Yheng Kok Shaohong Liang Choon Siang Tang Alex R Cook Lee Ching Ng Grace Yap Mapping dengue risk in Singapore using Random Forest. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
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.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. |
format |
Article in Journal/Newspaper |
author |
Janet Ong Xu Liu Jayanthi Rajarethinam Suet Yheng Kok Shaohong Liang Choon Siang Tang Alex R Cook Lee Ching Ng Grace Yap |
author_facet |
Janet Ong Xu Liu Jayanthi Rajarethinam Suet Yheng Kok Shaohong Liang Choon Siang Tang Alex R Cook Lee Ching Ng Grace Yap |
author_sort |
Janet Ong |
title |
Mapping dengue risk in Singapore using Random Forest. |
title_short |
Mapping dengue risk in Singapore using Random Forest. |
title_full |
Mapping dengue risk in Singapore using Random Forest. |
title_fullStr |
Mapping dengue risk in Singapore using Random Forest. |
title_full_unstemmed |
Mapping dengue risk in Singapore using Random Forest. |
title_sort |
mapping dengue risk in singapore using random forest. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2018 |
url |
https://doi.org/10.1371/journal.pntd.0006587 https://doaj.org/article/b224102494134548a94e965fb87e9b4d |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 12, Iss 6, p e0006587 (2018) |
op_relation |
http://europepmc.org/articles/PMC6023234?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0006587 https://doaj.org/article/b224102494134548a94e965fb87e9b4d |
op_doi |
https://doi.org/10.1371/journal.pntd.0006587 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
12 |
container_issue |
6 |
container_start_page |
e0006587 |
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1766341683013746688 |