Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model

Trends and Patterns of Systemic Glucocorticoid Prescription in Primary Care Institutions in Southwest China, from 2018 to 2021

Purpose

The purpose of this study was to investigate the prescribing patterns and usage trends of systemic glucocorticoids in primary care institutions located in Southwest China from 2018 to 2021.

Materials and Methods

A retrospective cross-sectional analysis of systemic glucocorticoids prescriptions was conducted in 32 primary care institutions located in Southwest China between 2018 and 2021. Prescriptions of systemic glucocorticoids were classified as appropriate or inappropriate use. Inappropriate use was further classified into (1) inappropriate indications and (2) inappropriate selection of glucocorticoids. Generalized estimation equations were employed to investigate the factors associated with inappropriate utilization of systemic glucocorticoids. The seasonal autoregressive integrated moving average (SARIMA) model was employed to predict the rate of inappropriate glucocorticoids prescriptions.

Results

A total of 203,846 (92.89%) prescriptions were included, both the number of systemic glucocorticoids prescriptions and inappropriate prescriptions increased in winter. Diseases of the respiratory system (68.90%) were the most frequent targets of systemic glucocorticoids use. Of all prescriptions, 73.18% exhibited inappropriate indications, while 0.05% demonstrated inappropriate selection. The utilization of systemic glucocorticoids was deemed inappropriate for diseases of the respiratory system (94.19%), followed by diseases of the digestive system (87.75%). Physicians, who were female or younger than 33 years old, possess lower levels of education and professional titles and exhibit a higher likelihood of inappropriately prescribing systemic glucocorticoids. The phenomenon of inappropriate glucocorticoids use was commoner among male patients aged 65 years and older. After conducting model verification, it was determined that the SARIMA model could be used to predict the monthly rate of inappropriate systemic glucocorticoids prescriptions in primary care institutions in southwest China.

Conclusion

The inappropriate use of systemic glucocorticoids remains a significant concern in primary care institutions. In this regard, continuing education and professional knowledge training of physicians should be strengthened in the future.

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis

BACKGROUND

Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain.

OBJECTIVE

Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China.

METHODS

ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances.

RESULTS

The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI -0.24% to 6.96%), and 2.16% (95% CI -0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values.

CONCLUSIONS

This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

BACKGROUND

This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD.

METHODS

We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis.

RESULTS

The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)_[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively.

CONCLUSIONS

Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.