OBJECTIVES: The prediction of dengue outbreaks is a critical concern in many countries. However, the setup of an ideal prediction system requires establishing numerous monitoring stations and performing data analysis, which are costly, time-consuming, and may not achieve the desired results. In this study, we developed a novel method for predicting impending dengue fever outbreaks several weeks prior to their occurrence. METHODS: By reversing moving approximate entropy algorithm and pattern recognition on time series compiled from the weekly case registry of the Center for Disease Control, Taiwan, 1998-2010, we compared the efficiencies of two patterns for predicting the outbreaks of dengue fever. RESULTS: The sensitivity of this method is 0.68, and the specificity is 0.54 using Pattern A to make predictions. Pattern B had a sensitivity of 0.90 and a specificity of 0.46. Patterns A and B make predictions 3.1 ± 2.2 weeks and 2.9 ± 2.4 weeks before outbreaks, respectively. CONCLUSIONS: Combined with pattern recognition, reversed moving approximate entropy algorithm on the time series built from weekly case registry is a promising tool for predicting the outbreaks of dengue fever.
OBJECTIVES: The prediction of dengue outbreaks is a critical concern in many countries. However, the setup of an ideal prediction system requires establishing numerous monitoring stations and performing data analysis, which are costly, time-consuming, and may not achieve the desired results. In this study, we developed a novel method for predicting impending dengue fever outbreaks several weeks prior to their occurrence. METHODS: By reversing moving approximate entropy algorithm and pattern recognition on time series compiled from the weekly case registry of the Center for Disease Control, Taiwan, 1998-2010, we compared the efficiencies of two patterns for predicting the outbreaks of dengue fever. RESULTS: The sensitivity of this method is 0.68, and the specificity is 0.54 using Pattern A to make predictions. Pattern B had a sensitivity of 0.90 and a specificity of 0.46. Patterns A and B make predictions 3.1 ± 2.2 weeks and 2.9 ± 2.4 weeks before outbreaks, respectively. CONCLUSIONS: Combined with pattern recognition, reversed moving approximate entropy algorithm on the time series built from weekly case registry is a promising tool for predicting the outbreaks of dengue fever.
Authors: Anna L Buczak; Benjamin Baugher; Linda J Moniz; Thomas Bagley; Steven M Babin; Erhan Guven Journal: PLoS One Date: 2018-01-03 Impact factor: 3.240
Authors: Shihab Uddin Chowdhury; Sanjana Sayeed; Iktisad Rashid; Md Golam Rabiul Alam; Abdul Kadar Muhammad Masum; M Ali Akber Dewan Journal: J Imaging Date: 2022-08-26