Using time-series analysis to predict disease counts with structural trend changes
作者：
Highlights：
• Disease count time-series prediction is difficult due to unexpected trend change.
• Analytics literature suggests a method that targets smooth changes.
• Disease count may encounter abrupt trend changes.
• The current paper addresses the abrupt changes in disease count data.
• The method has been evaluated through disease count data in Nevada.
摘要
•Disease count time-series prediction is difficult due to unexpected trend change.•Analytics literature suggests a method that targets smooth changes.•Disease count may encounter abrupt trend changes.•The current paper addresses the abrupt changes in disease count data.•The method has been evaluated through disease count data in Nevada.
论文关键词：Time-series analysis,Structural trend change,Disease counts
论文评审过程：Received 18 June 2018, Revised 7 November 2018, Accepted 10 November 2018, Available online 10 January 2019, Version of Record 10 January 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2018.11.004
