MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||4|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
Application of Wavelet Analysis Model Based on Hilbert Transform in Measles Outbreak Period
School of Mathematical Sciences, Ocean University of China, Qingdao, China
2 College of engineering, Ocean University of China, Qingdao, China
* Corresponding author: firstname.lastname@example.org
Objective To study the trend of cycle activity of measles epidemic from 1950 to 2014 and establish a model to predict the national incidence of measles in the future. Methods Using the national measles monitoring data from 1950 to 2014, we establish a information database. Then, we set up the wavelet analysis model based on Hilbert transform to study the cycle of measles incidence. Finally, we establish the ARIMA model of measles risk level to predict the incidence of measles by SPSS software. Results Wavelet analysis shows that the outbreak cycle of the incidence of measles is getting longer in the time dimension. ARIMA model analysis shows that national incidence of measles will fluctuate and decline in the next 36 years, which is may related to the improvement of medical standards and people’s awareness of the measles prevention. Conclusions The national incidence of measles is declining. It is cyclical and its outbreak cycle is getting longer. Data shows that the incidence of measles will gradually decrease in the future, and gradually achieve the global goal of eliminating measles.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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