Taking the COVID-19 data from 2020-1-23 to 3-21 days released by the China Health Protection Committee as the sample,the hospital remaining rate,mortality rate and cure rate are selected as covariates,and the contact ...Taking the COVID-19 data from 2020-1-23 to 3-21 days released by the China Health Protection Committee as the sample,the hospital remaining rate,mortality rate and cure rate are selected as covariates,and the contact infection rate is used as response variable to establish a high precision ADL model,results of return substitution show that the predicted value of contact infection rate almost coincides with the sample value,and shows three stages of change characteristics.After March 1,2020,the overall trend is downward,stable below 12%.Main influencing factors of contact infection rate are analyzed quantitatively.Based on this,the ARIMA(1,2,2)model is established to analyze and predict the mortality change trend.The results showed that the domestic COVID-19 mortality rate is stable near 4%after 2020-3-27.展开更多
基金funded by"Analysis of the Influence Mechanism of Modern Service Industry in Yunnan Province Based on Bayes Method"on the Project of Yunnan University Joint Fund.(2017FH001-068).
文摘Taking the COVID-19 data from 2020-1-23 to 3-21 days released by the China Health Protection Committee as the sample,the hospital remaining rate,mortality rate and cure rate are selected as covariates,and the contact infection rate is used as response variable to establish a high precision ADL model,results of return substitution show that the predicted value of contact infection rate almost coincides with the sample value,and shows three stages of change characteristics.After March 1,2020,the overall trend is downward,stable below 12%.Main influencing factors of contact infection rate are analyzed quantitatively.Based on this,the ARIMA(1,2,2)model is established to analyze and predict the mortality change trend.The results showed that the domestic COVID-19 mortality rate is stable near 4%after 2020-3-27.
