Impact Factor 2018: 1.772 (@Clarivate Analytics)
5-Year Impact Factor: 1.772 (@Clarivate Analytics)
  • Users Online: 586
  • Print this page
  • Email this page
ORIGINAL ARTICLE
Year : 2019  |  Volume : 12  |  Issue : 2  |  Page : 60-66

Modeling and predicting dengue fever cases in key regions of the Philippines using remote sensing data


1 Department of Medical Technology, Faculty of Pharmacy; Research Center for the Natural and Applied Sciences; The Graduate School, University of Santo Tomas, Manila City, Philippines
2 Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, Manila City, Philippines
3 Vietnam National Space Center; Vietnam Academy of Science and Technology, Hoang Quoc Viet, Hanoi, Vietnam

Correspondence Address:
Maria Ruth B. Pineda-Cortel
Department of Medical Technology, Faculty of Pharmacy, University of Santo Tomas, España Blvd, Sampaloc, Manila, Metro Manila
Philippines
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1995-7645.250838

Get Permissions

Objective: To correlate climatic and environmental factors such as land surface temperature, rainfall, humidity and normalized difference vegetation index with the incidence of dengue to develop prediction models for the Philippines using remote-sensing data. Methods: Time-series analysis was performed using dengue cases in four regions of the Philippines and monthly climatic variables extracted from Global Satellite Mapping of Precipitation for rainfall, and MODIS for the land surface temperature and normalized difference vegetation index from 2008-2015. Consistent dataset during the period of study was utilized in Autoregressive Integrated Moving Average models to predict dengue incidence in the four regions being studied. Results: The best-fitting models were selected to characterize the relationship between dengue incidence and climate variables. The predicted cases of dengue for January to December 2015 period fitted well with the actual dengue cases of the same timeframe. It also showed significantly good linear regression with a square of correlation of 0.869 5 for the four regions combined. Conclusion: Climatic and environmental variables are positively associated with dengue incidence and suit best as predictor factors using Autoregressive Integrated Moving Average models. This finding could be a meaningful tool in developing an early warning model based on weather forecasts to deliver effective public health prevention and mitigation programs.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed2647    
    Printed187    
    Emailed0    
    PDF Downloaded534    
    Comments [Add]    
    Cited by others 1    

Recommend this journal