A time-series prediction and analysis on rural inpatient with cardio-cerebrovascular disease in Wugang
10.16462/j.cnki.zhjbkz.2019.02.020
- Author:
Yu-pan WU
1
;
Liu-yi WEI
;
Shuang WANG
;
Shan LU
;
Bo-rui HU
;
Fu-hui TA
;
Lei CHEN
;
Zong-fu MAO
Author Information
1. School of Health Sciences, Wuhan University, Wuhan 430071, China
- Publication Type:Research Article
- Keywords:
Cardio-cerebrovascular disease;
Time series analysis;
Autoregressive comprehensive moving average model;
Seasonality;
Prediction
- From:
Chinese Journal of Disease Control & Prevention
2019;23(2):222-226
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a predictive model for inpatients of cardio-cerebrovascular disease in rural areas of Wugang through time series analysis, and predict the changing trend of cardio-cerebrovascular disease, so as to offer guidance for the health care resources allocation and prevention and control of cardio-cerebrovascular disease. Methods The seasonal autoregressive integrated moving average model (SARIMA) was constructed based on the monthly number of cases of cardio-cerebrovascular disease in rural areas from January 2013 to December 2016 by Stata 14.0 software, and the predictive effect of the model was verified with the monthly number of inpatients of cardio-cerebrovascular disease in 2017. Results The final fitting model of inpatients of cardio-cerebrovascular disease was SARIMA (2, 1, 1)×(0, 1, 0)12. The residual sequence of the model was diagnosed. Results of Ljung-Box Q test showed that the residual sequence was white noise sequence (Q=11.12, P=0.68). In addition, the 2017 forecast was basically consistent with the observations, the overall relative error was around -1.2%. The results showed that the summer was the peak period of cardiovascular and cerebrovascular hospitalization. Conclusion SARIMA model can accurately predict the number of inpatients of cardio-cerebrovascular disease in Wugang, which can provide data support for the hospital administrator to rationally allocate medical resources in the cardiovascular according to the needs of cardio-cerebrovascular treatment in different months.
