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

• Published in: Asian Pacific Journal of Tropical Medicine
• Main Authors: Maria Ruth B. Pineda-Cortel, Benjie M Clemente, Pham Thi Thanh Nga
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wolters Kluwer Medknow Publications 2019
• Subjects: dengue fever; climate change; remote sensing data; autoregressive integrated moving; average models; Arctic medicine. Tropical medicine; RC955-962; Arctic; Climate change
• Online Access: https://doi.org/10.4103/1995-7645.250838; https://doaj.org/article/3c27493045bd4beebf44eaa456281a70

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.