The research of SARIMA model for prediction of hepatitis B in mainland China

Zhao, Dingtao; Zhang, Huiwu; Cao, Qing; Wang, Zhiyi; Zhang, Ruihua

doi:10.1097/md.0000000000029317

Cited by 6 publications

(6 citation statements)

References 43 publications

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“…The incidence of HB in Henan has cyclical and seasonal characteristics, with the highest incidence in March and the lowest incidence in September and February each year (23). This is consistent with research results in China and may be due to people's reluctance to seek medical attention during the Spring Festival, as along with missed, delayed, and misreported cases.…”

Section: Resultssupporting

confidence: 85%

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Li,

et al. 2024

Preprint

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Background There may be evidence that COVID-19 affects illness patterns. This study aimed to estimate epidemiological trends in China and to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications. Methods The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges. Results The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% ~ -31%) from January to March 2020, by 24% (95% CI: -29% ~ -17%) from January to June 2020, by 15% (95% CI: -19% ~ -9.2%) from January to December 2020, by 11% (95% CI: -15% ~ -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% ~ -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% ~ -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 ~ 115,391). Conclusions After COVID-19 intervention, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

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Section: Resultssupporting

confidence: 85%

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Li,

et al. 2024

Preprint

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“…70 At present, the SARIMA model has been extensively utilized for predicting future patient visit rates and disease incidence. [70][71][72][73] In our study, the SARIMA(0, 1, 0)×(1, 1, 0) 12 model accurately predicted the IGR with an average error of only 8.40%. As predicted by our model (Figure 6), winter is the peak of inappropriate systemic GCs prescribing.…”

Section: Discussionmentioning

confidence: 57%

“… 70 In practice, it is essential to gather sufficient time-series data for validating the established model with new actual values and continuously incorporating fresh actual values to enhance the prediction model of IGR that can accurately reflect real-world scenarios. 71 …”

Section: Discussionmentioning

confidence: 99%

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

Li,

Zeng,

Fan

et al. 2023

RMHP

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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.

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“…There are several time-series models, such as ARIMA, exponential smoothing, GARCH, VAR, and prophet models. However, ARIMA is one of the most classic time-series models and has been widely used to predict infectious diseases, including COVID-19[ 50 ], hepatitis B [ 28 ], tuberculosis [ 19 ], human brucellosis [ 51 ], HFMD [ 52 ], and pertussis [ 53 ]. SARIMA is a powerful forecasting tool in public health informatics [ 50 ] that provides an important reference for surveillance and early warning of infectious diseases.…”

Section: Discussionmentioning

confidence: 99%

“…Four major steps are involved in the construction of the SARIMA model [ 19 , 28 ]. The first step was to determine whether the time series were stationary.…”

Section: Methodsmentioning

confidence: 99%

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

Zhao¹,

Zhang²,

Zhang

et al. 2023

BMC Public Health

Self Cite

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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.

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The research of SARIMA model for prediction of hepatitis B in mainland China

Cited by 6 publications

References 43 publications

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

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

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

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