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Wang YB, Qing SY, Liang ZY, Ma C, Bai YC, Xu CJ. Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China. World J Gastroenterol 2023; 29:5716-5727. [PMID: 38075851 PMCID: PMC10701333 DOI: 10.3748/wjg.v29.i42.5716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/28/2023] [Accepted: 10/23/2023] [Indexed: 11/13/2023] Open
Abstract
BACKGROUND Hepatitis B (HB) and hepatitis C (HC) place the largest burden in China, and a goal of eliminating them as a major public health threat by 2030 has been set. Making more informed and accurate forecasts of their spread is essential for developing effective strategies, heightening the requirement for early warning to deal with such a major public health threat. AIM To monitor HB and HC epidemics by the design of a paradigmatic seasonal autoregressive fractionally integrated moving average (SARFIMA) for projections into 2030, and to compare the effectiveness with the seasonal autoregressive integrated moving average (SARIMA). METHODS Monthly HB and HC incidence cases in China were obtained from January 2004 to June 2023. Descriptive analysis and the Hodrick-Prescott method were employed to identify trends and seasonality. Two periods (from January 2004 to June 2022 and from January 2004 to December 2015, respectively) were used as the training sets to develop both models, while the remaining periods served as the test sets to evaluate the forecasting accuracy. RESULTS There were incidents of 23400874 HB cases and 3590867 HC cases from January 2004 to June 2023. Overall, HB remained steady [average annual percentage change (AAPC) = 0.44, 95% confidence interval (95%CI): -0.94-1.84] while HC was increasing (AAPC = 8.91, 95%CI: 6.98-10.88), and both had a peak in March and a trough in February. In the 12-step-ahead HB forecast, the mean absolute deviation (15211.94), root mean square error (18762.94), mean absolute percentage error (0.17), mean error rate (0.15), and root mean square percentage error (0.25) under the best SARFIMA (3, 0, 0) (0, 0.449, 2)12 were smaller than those under the best SARIMA (3, 0, 0) (0, 1, 2)12 (16867.71, 20775.12, 0.19, 0.17, and 0.27, respectively). Similar results were also observed for the 90-step-ahead HB, 12-step-ahead HC, and 90-step-ahead HC forecasts. The predicted HB incidents totaled 9865400 (95%CI: 7508093-12222709) cases and HC totaled 1659485 (95%CI: 856681-2462290) cases during 2023-2030. CONCLUSION Under current interventions, China faces enormous challenges to eliminate HB and HC epidemics by 2030, and effective strategies must be reinforced. The integration of SARFIMA into public health for the management of HB and HC epidemics can potentially result in more informed and efficient interventions, surpassing the capabilities of SARIMA.
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Affiliation(s)
- Yong-Bin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Si-Yu Qing
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Zi-Yue Liang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Chang Ma
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Yi-Chun Bai
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Chun-Jie Xu
- Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China
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Lin S, Han L, Li D, Wang T, Wu Z, Zhang H, Xiao Z, Wu Y, Huang J, Wang M, Zhu Y. The Association between Meteorological Factors and the Prevalence of Acute-on-chronic Liver Failure: A Population-based Study, 2007-2016. J Clin Transl Hepatol 2019; 7:341-345. [PMID: 31915603 PMCID: PMC6943211 DOI: 10.14218/jcth.2019.00044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/13/2019] [Accepted: 12/06/2019] [Indexed: 12/04/2022] Open
Abstract
Background and Aims: The aim of this study was to investigate the effect(s) of meteorological factors on the prevalence of acute-on-chronic liver failure (ACLF) based on 10-years' worth of population data. Methods: We retrospectively collected ACLF case data from January 2007 to December 2016 from three major hospitals in Fuzhou City, China. Climatic data, including rainfall, mean temperature, differences in temperature (delta temperature) and mean humidity for each month were downloaded from the China Climatic Data Service Center. Following data collection, Poisson regression analysis was used to estimate the effect(s) of climatic factors on the risk of the prevalence of ACLF. Results: The population consisted of a total of 3510 cases, with a mean age of 44.7 ± 14.8 years-old and with 79.8% being male. Upon analyzing the population data, we found a growing trend and seasonal pattern of monthly counts of ACLF-related hospitalization throughout the past decade. Specifically, the primary peak of ACLF prevalence was in January and the secondary peak was in July. Poisson regression showed mean temperature (risk ratio = 0.991, 95%CI = 0.986-0.996) and mean humidity (risk ratio = 1.011, 95%CI = 1.006-1.017) to be independently correlated with the monthly cases of ACLF. The results suggest that every unit increase of mean temperature (1°C) and mean humidity (1%) are associated with 0.991- and 1.011-fold changes of ACLF cases, respectively. Rainfall and delta temperature did not appear to affect the prevalence of this disease. Conclusions: The hospitalization for ACLF peaks in January and July. Low temperature and high humidity appear to function as factors contributing to this seasonal pattern.
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Affiliation(s)
- Su Lin
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Lifen Han
- Department of Infectious Disease, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Dongliang Li
- Department of Hepatobiliary Disease, 900 Hospital of PLA, Fuzhou, Fujian, China
| | - Ting Wang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zimu Wu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Haoyang Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | | | - Yinlian Wu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jiaofeng Huang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Mingfang Wang
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Yueyong Zhu
- Liver Research Center of the First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Correspondence to: Yueyong Zhu, Department of Liver Research Center, First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian 350001, China. Tel: +86-591-87981656, Fax: +86-591-87982526, E-mail:
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Wang YW, Shen ZZ, Jiang Y. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. BMJ Open 2019; 9:e025773. [PMID: 31209084 PMCID: PMC6589045 DOI: 10.1136/bmjopen-2018-025773] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China. DESIGN Time-series study. SETTING The People's Republic of China. METHODS Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series. RESULTS The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12 model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684). CONCLUSIONS The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.
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Affiliation(s)
- Ya-wen Wang
- School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhong-zhou Shen
- School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- School of Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Li Z, Wang Z, Song H, Liu Q, He B, Shi P, Ji Y, Xu D, Wang J. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infect Drug Resist 2019; 12:1011-1020. [PMID: 31118707 PMCID: PMC6501557 DOI: 10.2147/idr.s190418] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 04/04/2019] [Indexed: 12/17/2022] Open
Abstract
Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1)12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.
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Affiliation(s)
- Zhongqi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Zhizhong Wang
- Department of Epidemiology and Health Statistic, School of Public Health, NingXia Medical University, Yinchuan, People's Republic of China
| | - Huan Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Biyu He
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Peiyi Shi
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Ye Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Dian Xu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Jianming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.,Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
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Huang D, Wu Z. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization. PLoS One 2017; 12:e0172539. [PMID: 28222194 PMCID: PMC5319685 DOI: 10.1371/journal.pone.0172539] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Accepted: 02/05/2017] [Indexed: 11/18/2022] Open
Abstract
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
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Affiliation(s)
- Daizheng Huang
- Department of Biomedical Engineering, School of Preclinical Medicine, Guangxi Medical University, Nanning, Guangxi Province, China
- * E-mail:
| | - Zhihui Wu
- Department of Biomedical Engineering, School of Preclinical Medicine, Guangxi Medical University, Nanning, Guangxi Province, China
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