Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China.

Purpose: To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models.

Methods: A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database.

ARIMA model for predicting chronic kidney disease and estimating its economic burden in China.

Background: Chronic kidney disease (CKD) is an important global public health issue. In China, CKD affects a large number of patients and causes a huge economic burden. This study provided a new way to predict the number of patients with CKD and estimate its economic burden in China based on the autoregressive integrated moving average (ARIMA) model.