Next Article in Journal
Stratified Template Matching to Support Refugee Camp Analysis in OBIA Workflows
Next Article in Special Issue
Analyzing the Potential Risk of Climate Change on Lyme Disease in Eastern Ontario, Canada Using Time Series Remotely Sensed Temperature Data and Tick Population Modelling
Previous Article in Journal
A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation
Previous Article in Special Issue
Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(4), 328; doi:10.3390/rs9040328

Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees

1
Department of Environmental Health Science, Fairbanks School of Public Health, Indiana University, IUPUI, Indianapolis, IN 46202, USA
2
Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, IN 46202, USA
3
Department of Geography, Indiana University, IUPUI, Indianapolis, IN 46202, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Bing Xu, Nils Chr. Stenseth, Ioannis Gitas and Prasad S. Thenkabail
Received: 18 October 2016 / Revised: 16 March 2017 / Accepted: 24 March 2017 / Published: 30 March 2017
(This article belongs to the Special Issue Remote Sensing Applications to Human Health)
View Full-Text   |   Download PDF [1371 KB, uploaded 30 March 2017]   |  

Abstract

Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs. View Full-Text
Keywords: Dengue; boosted regression tree; Aedes aegypti; remote sensing; GIS; vector modeling; neglected tropical diseases Dengue; boosted regression tree; Aedes aegypti; remote sensing; GIS; vector modeling; neglected tropical diseases
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ashby, J.; Moreno-Madriñán, M.J.; Yiannoutsos, C.T.; Stanforth, A. Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and Boosted Regression Trees. Remote Sens. 2017, 9, 328.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top