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Wang Z, Zhang W, Wu T, Lu N, He J, Wang J, Rao J, Gu Y, Cheng X, Li Y, Qi Y. Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China. Infect Dis Model 2024; 9:224-233. [PMID: 38303992 PMCID: PMC10831807 DOI: 10.1016/j.idm.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/19/2023] [Accepted: 01/11/2024] [Indexed: 02/03/2024] Open
Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013-2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS.
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Affiliation(s)
- Zixu Wang
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
- Bengbu Medical College, Bengbu, Anhui province, 233030, China
| | - Wenyi Zhang
- Chinese PLA Center for Disease Control and Prevention, Beijing, 100071, China
| | - Ting Wu
- Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu province, 210002, China
| | - Nianhong Lu
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
| | - Junyu He
- Ocean College, Zhejiang University, Zhoushan, 316021, China
- Ocean Academy, Zhejiang University, Zhoushan, 316021, China
| | - Junhu Wang
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
| | - Jixian Rao
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
| | - Yuan Gu
- Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu province, 210002, China
| | - Xianxian Cheng
- Bengbu Medical College, Bengbu, Anhui province, 233030, China
| | - Yuexi Li
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
| | - Yong Qi
- Pest Control Department, Huadong Research Institute for Medicine and Biotechniques, Nanjing, Jiangsu province, 210002, China
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Wang Z, Yang C, Li B, Wu H, Xu Z, Feng Z. Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models. Front Public Health 2024; 12:1365942. [PMID: 38496387 PMCID: PMC10941340 DOI: 10.3389/fpubh.2024.1365942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country. Methods We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model. Results Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models). Conclusion The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.
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Affiliation(s)
- ZhenDe Wang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - ChunXiao Yang
- School of Public Health, Shandong Second Medical University, Weifang, China
| | - Bing Li
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - HongTao Wu
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhen Xu
- Chinese Center for Disease Control and Prevention, Beijing, China
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China
| | - ZiJian Feng
- Chinese Preventive Medicine Association, Beijing, China
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Luo T, Zhou J, Yang J, Xie Y, Wei Y, Mai H, Lu D, Yang Y, Cui P, Ye L, Liang H, Huang J. 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. J Med Internet Res 2023; 25:e49400. [PMID: 37902815 PMCID: PMC10644180 DOI: 10.2196/49400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/23/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
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.
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Affiliation(s)
- Tingyan Luo
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Jie Zhou
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Jing Yang
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Yulan Xie
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Yiru Wei
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Huanzhuo Mai
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Dongjia Lu
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Yuecong Yang
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Ping Cui
- Life Science Institute, Guangxi Medical University, Nanning, China
| | - Li Ye
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
| | - Hao Liang
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
- Life Science Institute, Guangxi Medical University, Nanning, China
| | - Jiegang Huang
- School of Public Health, Guangxi Medical University, Nanning, China
- Guangxi Key Laboratory of AIDS Prevention and Treatment, Guangxi Medical University, Nanning, China
- Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Guangxi Medical University, Nanning, China
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Zhao D, Zhang H, Zhang R, He S. Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China. BMC Public Health 2023; 23:619. [PMID: 37003988 PMCID: PMC10064964 DOI: 10.1186/s12889-023-15543-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
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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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, People's Republic of China
| | - Huiwu Zhang
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, People's Republic of China.
| | - Ruihua Zhang
- School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, People's Republic of China.
- General Practitioners Training Center of Sichuan Province, Chengdu, Sichuan, People's Republic of China.
| | - Sizhang He
- Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, 64600, Sichuan, China
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Liang W, Hu A, Hu P, Zhu J, Wang Y. Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:55-65. [PMID: 36271168 DOI: 10.1007/s00484-022-02385-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 09/30/2022] [Accepted: 10/06/2022] [Indexed: 06/16/2023]
Abstract
Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β = - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β = - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β = - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.
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Affiliation(s)
- Wenjuan Liang
- Department of Epidemiology, International School of Public Health and One Health, Hainan Medical University, Haikou, Hainan Province, 571199, People's Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Ailing Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Pan Hu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Jinqin Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, The Third Affiliated Hospital, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People's Republic of China.
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Ma Y, Gao S, Kang Z, Shan L, Jiao M, Li Y, Liang L, Hao Y, Zhao B, Ning N, Gao L, Cui Y, Sun H, Wu Q, Liu H. Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis. Front Public Health 2022; 10:923318. [PMID: 36589977 PMCID: PMC9799716 DOI: 10.3389/fpubh.2022.923318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Objective Over the past decade, scarlet fever has caused a relatively high economic burden in various regions of China. Non-pharmaceutical interventions (NPIs) are necessary because of the absence of vaccines and specific drugs. This study aimed to characterize the demographics of patients with scarlet fever, describe its spatiotemporal distribution, and explore the impact of NPIs on the disease in the era of coronavirus disease 2019 (COVID-19) in China. Methods Using monthly scarlet fever data from January 2011 to December 2019, seasonal autoregressive integrated moving average (SARIMA), advanced innovation state-space modeling framework that combines Box-Cox transformations, Fourier series with time-varying coefficients, and autoregressive moving average error correction method (TBATS) models were developed to select the best model for comparing between the expected and actual incidence of scarlet fever in 2020. Interrupted time series analysis (ITSA) was used to explore whether NPIs have an effect on scarlet fever incidence, while the intervention effects of specific NPIs were explored using correlation analysis and ridge regression methods. Results From 2011 to 2017, the total number of scarlet fever cases was 400,691, with children aged 0-9 years being the main group affected. There were two annual incidence peaks (May to June and November to December). According to the best prediction model TBATS (0.002, {0, 0}, 0.801, {<12, 5>}), the number of scarlet fever cases was 72,148 and dual seasonality was no longer prominent. ITSA showed a significant effect of NPIs of a reduction in the number of scarlet fever episodes (β2 = -61526, P < 0.005), and the effect of canceling public events (c3) was the most significant (P = 0.0447). Conclusions The incidence of scarlet fever during COVID-19 was lower than expected, and the total incidence decreased by 80.74% in 2020. The results of this study indicate that strict NPIs may be of potential benefit in preventing scarlet fever occurrence, especially that related to public event cancellation. However, it is still important that vaccines and drugs are available in the future.
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Affiliation(s)
- Yunxia Ma
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Shanshan Gao
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Zheng Kang
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Linghan Shan
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Mingli Jiao
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Ye Li
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Libo Liang
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Yanhua Hao
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Binyu Zhao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Ning Ning
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Lijun Gao
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Yu Cui
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Hong Sun
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China
| | - Qunhong Wu
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China,*Correspondence: Qunhong Wu
| | - Huan Liu
- Department of Social Medicine, Health Management College, Harbin Medical University, Harbin, China,Huan Liu
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Donadel M, Scobie HM, Pastore R, Grabovac V, Batmunkh N, O’Connor S, Dahl BA, Murrill CS. Comprehensive Vaccine-Preventable Disease Surveillance in the Western Pacific Region: A Literature Review on Integration of Surveillance Functions, 2000-2021. GLOBAL HEALTH, SCIENCE AND PRACTICE 2022; 10:GHSP-D-22-00017. [PMID: 36316133 PMCID: PMC9622275 DOI: 10.9745/ghsp-d-22-00017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 10/04/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION A strategic framework for 2021-2030 developed by the World Health Organization (WHO) Regional Office for the Western Pacific emphasizes the need for high-quality and integrated vaccine-preventable disease (VPD) surveillance. We conducted a literature review to document the barriers, enabling factors, and innovations for integrating surveillance functions for VPDs and other communicable diseases in Western Pacific Region (WPR) countries. METHODS We searched published and gray literature on integrated VPD surveillance from 2000 to 2021. Articles in English, Spanish, or French were screened to identify those relating to VPD surveillance in a WPR country and not meeting defined exclusion criteria. We categorized articles using the 8 WHO surveillance support functions and abstracted data on the country; type of surveillance; and reported barriers, enabling factors, and best practices for integration. RESULTS Of the 3,137 references screened, 87 met the eligibility criteria. Of the 8 surveillance support functions, the proportion of references that reported integration related to the laboratory was 56%, followed by workforce capacity (54%), governance (51%), data management and use (47%), field logistics and communication (47%), coordination (15%), program management (13%), and supervision (9%). Several references noted fragmented systems and a lack of coordination between units as barriers to integration, highlighting the importance of engagement across public health units and between the public and private sectors. The literature also indicated a need for interoperable information systems and revealed the use of promising new technologies for data reporting and laboratory testing. In some WPR countries, workforce capacity was strengthened at all administrative levels by the implementation of integrated trainings on data monitoring and use and on laboratory techniques applicable to multiple VPDs. CONCLUSION This literature review supports integrating VPDs into broader communicable disease surveillance systems in WPR countries while ensuring that the minimal WHO-recommended standards for VPD surveillance are met.
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Affiliation(s)
- Morgane Donadel
- Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA.,Correspondence to Morgane Donadel ()
| | - Heather M. Scobie
- Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Roberta Pastore
- World Health Organization, Western Pacific Regional Office, Manila, the Philippines
| | - Varja Grabovac
- World Health Organization, Western Pacific Regional Office, Manila, the Philippines
| | - Nyambat Batmunkh
- World Health Organization, Western Pacific Regional Office, Manila, the Philippines
| | - Stephanie O’Connor
- Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Benjamin A. Dahl
- Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Christopher S. Murrill
- Global Immunization Division, U.S. Centers for Disease Control and Prevention, Atlanta, GA, USA
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Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models. Sci Rep 2022; 12:18138. [PMID: 36307471 PMCID: PMC9614203 DOI: 10.1038/s41598-022-23154-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 10/25/2022] [Indexed: 12/30/2022] Open
Abstract
Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.
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Zhao D, Zhang H, Cao Q, Wang Z, Zhang R. The research of SARIMA model for prediction of hepatitis B in mainland China. Medicine (Baltimore) 2022; 101:e29317. [PMID: 35687775 PMCID: PMC9276452 DOI: 10.1097/md.0000000000029317] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 04/29/2022] [Indexed: 01/04/2023] Open
Abstract
Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures.Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model.Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval.This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.
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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, China
| | - Huiwu Zhang
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, China
| | - Qing Cao
- Department of Medical Administration, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Zhiyi Wang
- Department of Medical Administration, Sichuan Cancer Hospital & Institute,Chengdu, Sichuan, China
| | - Ruihua Zhang
- School of Management,Chengdu University of Traditional Chinese Medicine,Chengdu, Sichuan, China
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Necesito IV, Velasco JMS, Jung J, Bae YH, Yoo Y, Kim S, Kim HS. Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices. Front Public Health 2022; 10:871354. [PMID: 35719622 PMCID: PMC9204014 DOI: 10.3389/fpubh.2022.871354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Most coronavirus disease 2019 (COVID-19) models use a combination of agent-based and equation-based models with only a few incorporating environmental factors in their prediction models. Many studies have shown that human and environmental factors play huge roles in disease transmission and spread, but few have combined the use of both factors, especially for SARS-CoV-2. In this study, both man-made policies (Stringency Index) and environment variables (Niño SST Index) were combined to predict the number of COVID-19 cases in South Korea. The performance indicators showed satisfactory results in modeling COVID-19 cases using the Non-linear Autoregressive Exogenous Model (NARX) as the modeling method, and Stringency Index (SI) and Niño Sea Surface Temperature (SST) as model variables. In this study, we showed that the accuracy of SARS-CoV-2 transmission forecasts may be further improved by incorporating both the Niño SST and SI variables and combining these variables with NARX may outperform other models. Future forecasting work by modelers should consider including climate or environmental variables (i.e., Niño SST) to enhance the prediction of transmission and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Imee V. Necesito
- Department of Civil Engineering, Inha University, Incheon, South Korea
- *Correspondence: Imee V. Necesito
| | - John Mark S. Velasco
- Department of Clinical Epidemiology, College of Medicine, University of the Philippines, Manila, Philippines
- Institute of Molecular Biology and Biotechnology, National Institutes of Health, University of the Philippines, Manila, Philippines
| | - Jaewon Jung
- Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi-do, South Korea
| | - Young Hye Bae
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Younghoon Yoo
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Soojun Kim
- Department of Civil Engineering, Inha University, Incheon, South Korea
| | - Hung Soo Kim
- Department of Civil Engineering, Inha University, Incheon, South Korea
- Hung Soo Kim
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Qiu H, Zhao H, Xiang H, Ou R, Yi J, Hu L, Zhu H, Ye M. Forecasting the incidence of mumps in Chongqing based on a SARIMA model. BMC Public Health 2021; 21:373. [PMID: 33596871 PMCID: PMC7890879 DOI: 10.1186/s12889-021-10383-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 02/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Mumps is classified as a class C infection disease in China, and the Chongqing area has one of the highest incidence rates in the country. We aimed to establish a prediction model for mumps in Chongqing and analyze its seasonality, which is important for risk analysis and allocation of resources in the health sector. METHODS Data on incidence of mumps from January 2004 to December 2018 were obtained from Chongqing Municipal Bureau of Disease Control and Prevention. The incidence of mumps from 2004 to 2017 was fitted using a seasonal autoregressive comprehensive moving average (SARIMA) model. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to compare the goodness of fit of the models. The 2018 incidence data were used for validation. RESULTS From 2004 to 2018, a total of 159,181 cases (93,655 males and 65,526 females) of mumps were reported in Chongqing, with significantly more men than women. The age group of 0-19 years old accounted for 92.41% of all reported cases, and students made up the largest proportion (62.83%), followed by scattered children and children in kindergarten. The SARIMA(2, 1, 1) × (0, 1, 1)12 was the best fit model, RMSE and MAPE were 0.9950 and 39.8396%, respectively. CONCLUSION Based on the study findings, the incidence of mumps in Chongqing has an obvious seasonal trend, and SARIMA(2, 1, 1) × (0, 1, 1)12 model can also predict the incidence of mumps well. The SARIMA model of time series analysis is a feasible and simple method for predicting mumps in Chongqing.
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Affiliation(s)
- Hongfang Qiu
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
| | - Han Zhao
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042 China
| | - Haiyan Xiang
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
| | - Rong Ou
- Department of Medical Informatics Library, Chongqing Medical University, Chongqing, 400016 China
| | - Jing Yi
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
| | - Ling Hu
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
| | - Hua Zhu
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
| | - Mengliang Ye
- Department of Epidemiology and Health Statistics, School of Public Health and Management, Chongqing Medical University, Chongqing, 400016 China
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12
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Wang Y, Xu C, Ren J, Li Y, Wu W, Yao S. Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:7281-7294. [PMID: 33026621 DOI: 10.1007/s11356-020-11072-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/30/2020] [Indexed: 06/11/2023]
Abstract
The scarlet fever incidence has increased drastically in recent years in China. However, the long-term relationship between climate variation and scarlet fever remains contradictory, and an early detection system is lacking. In this study, we aim to explore the potential long-term effects of variations in monthly climatic parameters on scarlet fever and to develop an early scarlet-fever detection tool. Data comprising monthly scarlet fever cases and monthly average climatic variables from 2004 to 2017 were retrieved from the Notifiable Infectious Disease Surveillance System and National Meteorological Science Center, respectively. We used a negative binomial multivariable regression to assess the long-term impacts of weather parameters on scarlet fever and then built a novel forecasting technique by integrating an autoregressive distributed lag (ARDL) method with a nonlinear autoregressive neural network (NARNN) based on the significant meteorological drivers. Scarlet fever was a seasonal disease that predominantly peaked in spring and winter. The regression results indicated that a 1 °C increment in the monthly average temperature and a 1-h increment in the monthly aggregate sunshine hours were associated with 17.578% (95% CI 7.674 to 28.393%) and 0.529% (95% CI 0.035 to 1.025%) increases in scarlet fever cases, respectively; a 1-hPa increase in the average atmospheric pressure at a 1-month lag was associated with 12.996% (95% CI 9.972 to 15.919%) decrements in scarlet fever cases. Based on the model evaluation criteria, the best-performing basic and combined approaches were ARDL(1,0,0,1) and ARDL(1,0,0,1)-NARNN(5, 22), respectively, and this hybrid approach comprised smaller performance measures in both the training and testing stages than those of the basic model. Climate variability has a significant long-term influence on scarlet fever. The ARDL-NARNN technique with the incorporation of meteorological drivers can be used to forecast the future epidemic trends of scarlet fever. These findings may be of great help for the prevention and control of scarlet fever.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China.
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, People's Republic of China
| | - Jingchao Ren
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China
| | - Yuchun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui road, Hongqi District, Xinxiang City, 453003, Henan Province, People's Republic of China
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Wang Y, Xu C, Yao S, Zhao Y, Li Y, Wang L, Zhao X. Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India. Infect Drug Resist 2020; 13:3335-3350. [PMID: 33061481 PMCID: PMC7532899 DOI: 10.2147/idr.s265292] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 08/12/2020] [Indexed: 12/13/2022] Open
Abstract
Objective The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD,A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A,N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China
| | - Sanqiao Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Yingzheng Zhao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Yuchun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Lei Wang
- Center for Musculoskeletal Surgery, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Xiangmei Zhao
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
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Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis. Epidemiol Infect 2020; 148:e193. [PMID: 32807257 PMCID: PMC7482188 DOI: 10.1017/s095026882000182x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.
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Chen H, Chen Y, Sun B, Wen L, An X. Epidemiological study of scarlet fever in Shenyang, China. BMC Infect Dis 2019; 19:1074. [PMID: 31864293 PMCID: PMC6925867 DOI: 10.1186/s12879-019-4705-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Accepted: 12/12/2019] [Indexed: 11/29/2022] Open
Abstract
Background Since 2011, there has been an increase in the incidence of scarlet fever across China. The main objective of this study was to depict the spatiotemporal epidemiological characteristics of the incidence of scarlet fever in Shenyang, China, in 2018 so as to provide the scientific basis for effective strategies of scarlet control and prevention. Methods Excel 2010 was used to demonstrate the temporal distribution at the month level and ArcGIS10.3 was used to demonstrate the spatial distribution at the district/county level. Moran’s autocorrelation coefficient was used to examine the spatial autocorrelation and the Getis-Ord statistic was used to determine the hot-spot areas of scarlet fever. Results A total of 2314 scarlet fever cases were reported in Shenyang in 2018 with an annual incidence of 31.24 per 100,000. The incidence among males was higher than that among females(p<0.001). A vast majority of the cases (96.89%) were among children aged 3 to 11 years. The highest incidence was 625.34/100,000 in children aged 5–9 years. In 2018 there were two seasonal peaks of scarlet fever in June (summer-peak) and December (winter-peak). The incidence of scarlet fever in urban areas was significantly higher than that in rural areas(p<0.001). The incidence of scarlet fever was randomly distributed in Shenyang. There are hotspot areas located in seven districts. Conclusions Urban areas are the hot spots of scarlet fever and joint prevention and control measures between districts should be applied. Children aged 3–11 are the main source of scarlet fever and therefore the introduction of prevention and control into kindergarten and primary schools may be key to the control of scarlet fever epidemics.
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Affiliation(s)
- Huijie Chen
- Department of Infectious Disease, Shenyang Health Service and Administrative Law Enforcement Center (Shenyang Center for Disease Control and Prevention), Shenyang, 110031, China.
| | | | - Baijun Sun
- Department of Infectious Disease, Shenyang Health Service and Administrative Law Enforcement Center (Shenyang Center for Disease Control and Prevention), Shenyang, 110031, China
| | - Lihai Wen
- Department of Infectious Disease, Shenyang Health Service and Administrative Law Enforcement Center (Shenyang Center for Disease Control and Prevention), Shenyang, 110031, China
| | - Xiangdong An
- Department of Infectious Disease, Shenyang Health Service and Administrative Law Enforcement Center (Shenyang Center for Disease Control and Prevention), Shenyang, 110031, China
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Wang Y, Xu C, Zhang S, Wang Z, Yang L, Zhu Y, Yuan J. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open 2019; 9:e024409. [PMID: 31371283 PMCID: PMC6678063 DOI: 10.1136/bmjopen-2018-024409] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE Tuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity. METHODS We develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques. RESULTS In terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12 model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high. CONCLUSIONS The new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.
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Affiliation(s)
- Yongbin Wang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, China
| | - Shengkui Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Zhende Wang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Li Yang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Ying Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
| | - Juxiang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, China
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