Progress in the application of time series prediction models for disease monitoring
After the SARS-CoV-2 pandemic,how to use disease surveillance data to establish prediction and early warning is an important research topic in the field of disease surveillance.With the rapid development of computer technology,various emerging time series models have been rapidly increasing in recent years,but there is still a lack of an overview of various disease monitoring time series prediction models.This study reviews the main disease monitoring time series prediction models both domestically and internationally in recent years and provides readers with a basic understanding of the principles,classification methods,implementation steps,and model evaluation indicators of various disease monitoring time series prediction models.At the same time,this study introduces the main software commonly used for modeling,provides readers with a detailed and comprehensive introduction to the current application progress in disease monitoring time series prediction models both domestically and internationally,and provides important references for better establishing prediction and early warning models.
NETL
NSTL
万方数据
