Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.

• Main Authors: Naizhuo Zhao (Author), Katia Charland (Author), Mabel Carabali (Author), Elaine O Nsoesie (Author), Mathieu Maheu-Giroux (Author), Erin Rees (Author), Mengru Yuan (Author), Cesar Garcia Balaguera (Author), Gloria Jaramillo Ramirez (Author), Kate Zinszer (Author)
• Format: Book
• Published: Public Library of Science (PLoS), 2020-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level da...