Modeling tools for dengue risk mapping : a systematic review

Introduction: The global spread and the increased frequency and magnitude of epidemic dengue in the last 50 years underscore the urgent need for effective tools for surveillance, prevention, and control. This review aims at providing a systematic overview of what predictors are critical and which sp...

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Main Authors: Louis, Valérie R, Phalkey, Revati, Horstick, Olaf, Ratanawong, Pitcha, Wilder-Smith, Annelies, Tozan, Yesim, Dambach, Peter
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
Format: Article
Language:English
Published: 2015
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Online Access:https://hdl.handle.net/10356/79451
http://hdl.handle.net/10220/25390
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spelling sg-ntu-dr.10356-794512022-02-16T16:30:18Z Modeling tools for dengue risk mapping : a systematic review Louis, Valérie R Phalkey, Revati Horstick, Olaf Ratanawong, Pitcha Wilder-Smith, Annelies Tozan, Yesim Dambach, Peter Lee Kong Chian School of Medicine (LKCMedicine) DRNTU::Science::Biological sciences::Microbiology::Bacteria Introduction: The global spread and the increased frequency and magnitude of epidemic dengue in the last 50 years underscore the urgent need for effective tools for surveillance, prevention, and control. This review aims at providing a systematic overview of what predictors are critical and which spatial and spatio-temporal modeling approaches are useful in generating risk maps for dengue. Methods: A systematic search was undertaken, using the PubMed, Web of Science, WHOLIS, Centers for Disease Control and Prevention (CDC) and OvidSP databases for published citations, without language or time restrictions. A manual search of the titles and abstracts was carried out using predefined criteria, notably the inclusion of dengue cases. Data were extracted for pre-identified variables, including the type of predictors and the type of modeling approach used for risk mapping. Results: A wide variety of both predictors and modeling approaches was used to create dengue risk maps. No specific patterns could be identified in the combination of predictors or models across studies. The most important and commonly used predictors for the category of demographic and socio-economic variables were age, gender, education, housing conditions and level of income. Among environmental variables, precipitation and air temperature were often significant predictors. Remote sensing provided a source of varied land cover data that could act as a proxy for other predictor categories. Descriptive maps showing dengue case hotspots were useful for identifying high-risk areas. Predictive maps based on more complex methodology facilitated advanced data analysis and visualization, but their applicability in public health contexts remains to be established. Conclusions: The majority of available dengue risk maps was descriptive and based on retrospective data. Availability of resources, feasibility of acquisition, quality of data, alongside available technical expertise, determines the accuracy of dengue risk maps and their applicability to the field of public health. A large number of unknowns, including effective entomological predictors, genetic diversity of circulating viruses, population serological profile, and human mobility, continue to pose challenges and to limit the ability to produce accurate and effective risk maps, and fail to support the development of early warning systems. Published version 2015-04-13T07:23:43Z 2019-12-06T13:25:41Z 2015-04-13T07:23:43Z 2019-12-06T13:25:41Z 2014 2014 Journal Article Louis, V. R., Phalkey, R., Horstick, O., Ratanawong, P., Wilder-Smith, A., Tozan, Y., et al. (2014). Modeling tools for dengue risk mapping : a systematic review. International journal of health geographics, 13. 1476-072X https://hdl.handle.net/10356/79451 http://hdl.handle.net/10220/25390 10.1186/1476-072X-13-50 25487167 en International journal of health geographics © 2014 Louis et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 15 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Biological sciences::Microbiology::Bacteria
spellingShingle DRNTU::Science::Biological sciences::Microbiology::Bacteria
Louis, Valérie R
Phalkey, Revati
Horstick, Olaf
Ratanawong, Pitcha
Wilder-Smith, Annelies
Tozan, Yesim
Dambach, Peter
Modeling tools for dengue risk mapping : a systematic review
description Introduction: The global spread and the increased frequency and magnitude of epidemic dengue in the last 50 years underscore the urgent need for effective tools for surveillance, prevention, and control. This review aims at providing a systematic overview of what predictors are critical and which spatial and spatio-temporal modeling approaches are useful in generating risk maps for dengue. Methods: A systematic search was undertaken, using the PubMed, Web of Science, WHOLIS, Centers for Disease Control and Prevention (CDC) and OvidSP databases for published citations, without language or time restrictions. A manual search of the titles and abstracts was carried out using predefined criteria, notably the inclusion of dengue cases. Data were extracted for pre-identified variables, including the type of predictors and the type of modeling approach used for risk mapping. Results: A wide variety of both predictors and modeling approaches was used to create dengue risk maps. No specific patterns could be identified in the combination of predictors or models across studies. The most important and commonly used predictors for the category of demographic and socio-economic variables were age, gender, education, housing conditions and level of income. Among environmental variables, precipitation and air temperature were often significant predictors. Remote sensing provided a source of varied land cover data that could act as a proxy for other predictor categories. Descriptive maps showing dengue case hotspots were useful for identifying high-risk areas. Predictive maps based on more complex methodology facilitated advanced data analysis and visualization, but their applicability in public health contexts remains to be established. Conclusions: The majority of available dengue risk maps was descriptive and based on retrospective data. Availability of resources, feasibility of acquisition, quality of data, alongside available technical expertise, determines the accuracy of dengue risk maps and their applicability to the field of public health. A large number of unknowns, including effective entomological predictors, genetic diversity of circulating viruses, population serological profile, and human mobility, continue to pose challenges and to limit the ability to produce accurate and effective risk maps, and fail to support the development of early warning systems.
author2 Lee Kong Chian School of Medicine (LKCMedicine)
author_facet Lee Kong Chian School of Medicine (LKCMedicine)
Louis, Valérie R
Phalkey, Revati
Horstick, Olaf
Ratanawong, Pitcha
Wilder-Smith, Annelies
Tozan, Yesim
Dambach, Peter
format Article
author Louis, Valérie R
Phalkey, Revati
Horstick, Olaf
Ratanawong, Pitcha
Wilder-Smith, Annelies
Tozan, Yesim
Dambach, Peter
author_sort Louis, Valérie R
title Modeling tools for dengue risk mapping : a systematic review
title_short Modeling tools for dengue risk mapping : a systematic review
title_full Modeling tools for dengue risk mapping : a systematic review
title_fullStr Modeling tools for dengue risk mapping : a systematic review
title_full_unstemmed Modeling tools for dengue risk mapping : a systematic review
title_sort modeling tools for dengue risk mapping : a systematic review
publishDate 2015
url https://hdl.handle.net/10356/79451
http://hdl.handle.net/10220/25390
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