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Chen W, Luo H, Li J, Chi J. Long-term trend prediction of pandemic combining the compartmental and deep learning models. Sci Rep 2024; 14:21068. [PMID: 39256475 PMCID: PMC11387753 DOI: 10.1038/s41598-024-72005-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 09/02/2024] [Indexed: 09/12/2024] Open
Abstract
Predicting the spread trends of a pandemic is crucial, but long-term prediction remains challenging due to complex relationships among disease spread stages and preventive policies. To address this issue, we propose a novel approach that utilizes data augmentation techniques, compartmental model features, and disease preventive policies. We also use a breakpoint detection method to divide the disease spread into distinct stages and weight these stages using a self-attention mechanism to account for variations in virus transmission capabilities. Finally, we introduce a long-term spread trend prediction model for infectious diseases based on a bi-directional gated recurrent unit network. To evaluate the effectiveness of our model, we conducted experiments using public datasets, focusing on the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85-4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread.
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Affiliation(s)
- Wanghu Chen
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China.
| | - Heng Luo
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Jing Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Jiacheng Chi
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
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2
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Kim SI, Lee JW, Eun YG, Lee YC. A SEER-based analysis of trends in HPV-associated oropharyngeal squamous cell carcinoma. Infect Agent Cancer 2024; 19:29. [PMID: 38943144 PMCID: PMC11214209 DOI: 10.1186/s13027-024-00592-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 06/19/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND The proportional trends of HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) according to various factors have not been analyzed in detail in previous studies. We aimed to evaluate the trends of HPV-associated OPSCC in the United States. METHODS This retrospective cohort study included 13,081 patients with OPSCC from large population-based data using Surveillance, Epidemiology, and End Results (SEER) 2010-2017 database, 17 Registries. Patients were diagnosed with OPSCC primarily in the base of tongue (BOT), posterior pharyngeal wall (PPW), soft palate (SP), and tonsil and were tested for HPV infection status. We analyzed how the proportional trends of patients with OPSCC changed according to various demographic factors. Additionally, we forecasted and confirmed the trend of HPV (+) and (-) patients with OPSCC using the autoregressive integrated moving average (ARIMA) model. RESULTS The proportion of patients who performed the HPV testing increased every year, and it has exceeded 50% since 2014 (21.95% and 51.37% at 2010 and 2014, respectively). The HPV-positive rates tended to increase over past 7 years (66.37% and 79.32% at 2010 and 2016, respectively). Positivity rates of HPV were significantly higher in OPSCC located in the tonsil or BOT than in those located in PPW or SP. The ARIMA (2,1,0) and (0,1,0) models were applied to forecast HPV (+) and (-) patients with OPSCC, respectively, and the predicted data generally matched the actual data well. CONCLUSION This large population-based study suggests that the proportional trends of HPV (+) patients with OPSCC has increased and will continue to increase. However, the trends of HPV (+) and (-) patients differed greatly according to various demographic factors. These results present a direction for establishing appropriate preventive measures to deal with HPV-related OPSCC in more detail.
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Affiliation(s)
- Su Il Kim
- Department of Otolaryngology-Head and Neck Surgery, Kyung Hee University School of Medicine, Kyung Hee University Hospital at Gangdong, #892 Dongnamro, Gangdong-gu, Seoul, 05278, Korea
| | - Jung Woo Lee
- Department of Oral and Maxillofacial Surgery, School of Dentistry, Kyung Hee University, Seoul, Korea
| | - Young-Gyu Eun
- Department of Otolaryngology-Head and Neck Surgery, Kyung Hee University School of Medicine, Kyung Hee University Medical Center, Seoul, Korea
| | - Young Chan Lee
- Department of Otolaryngology-Head and Neck Surgery, Kyung Hee University School of Medicine, Kyung Hee University Hospital at Gangdong, #892 Dongnamro, Gangdong-gu, Seoul, 05278, Korea.
- Department of Age Service-Tech Convergence, College of Medicine, Kyung Hee University, Seoul, Korea.
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Yun Z, Li P, Wang J, Lin F, Li W, Weng M, Zhang Y, Wu H, Li H, Cai X, Li X, Fu X, Wu T, Gao Y. Spatial-temporal analysis of hepatitis E in Hainan Province, China (2013-2022): insights from four major hospitals. Front Public Health 2024; 12:1381204. [PMID: 38993698 PMCID: PMC11236752 DOI: 10.3389/fpubh.2024.1381204] [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: 02/11/2024] [Accepted: 06/13/2024] [Indexed: 07/13/2024] Open
Abstract
Objective Exploring the Incidence, Epidemic Trends, and Spatial Distribution Characteristics of Sporadic Hepatitis E in Hainan Province from 2013 to 2022 through four major tertiary hospitals in the Province. Methods We collected data on confirmed cases of hepatitis E in Hainan residents admitted to the four major tertiary hospitals in Haikou City from January 2013 to December 2022. We used SPSS software to analyze the correlation between incidence rate and economy, population density and geographical location, and origin software to draw a scatter chart and SAS 9.4 software to conduct a descriptive analysis of the time trend. The distribution was analyzed using ArcMap 10.8 software (spatial autocorrelation analysis, hotspot identification, concentration, and dispersion trend analysis). SAS software was used to build an autoregressive integrated moving average model (ARIMA) to predict the monthly number of cases in 2023 and 2024. Results From 2013 to 2022, 1,922 patients with sporadic hepatitis E were treated in the four hospitals of Hainan Province. The highest proportion of patients (n = 555, 28.88%) were aged 50-59 years. The annual incidence of hepatitis E increased from 2013 to 2019, with a slight decrease in 2020 and 2021 and an increase in 2022. The highest number of cases was reported in Haikou, followed by Dongfang and Danzhou. We found that there was a correlation between the economy, population density, latitude, and the number of cases, with the correlation coefficient |r| value fluctuating between 0.403 and 0.421, indicating a linear correlation. At the same time, a scatter plot shows the correlation between population density and incidence from 2013 to 2022, with r2 values fluctuating between 0.5405 and 0.7116, indicating a linear correlation. Global Moran's I, calculated through spatial autocorrelation analysis, showed that each year from 2013 to 2022 all had a Moran's I value >0, indicating positive spatial autocorrelation (p < 0.01). Local Moran's I analysis revealed that from 2013 to 2022, local hotspots were mainly concentrated in the northern part of Hainan Province, with Haikou, Wenchang, Ding'an, and Chengmai being frequent hotspot regions, whereas Baoting, Qiongzhong, and Ledong were frequent cold-spot regions. Concentration and dispersion analysis indicated a clear directional pattern in the average density distribution, moving from northeast to southwest. Time-series forecast modeling showed that the forecast number of newly reported cases per month remained relatively stable in 2023 and 2024, fluctuating between 17 and 19. Conclusion The overall incidence of hepatitis E in Hainan Province remains relatively stable. The incidence of hepatitis E in Hainan Province increased from 2013 to 2019, with a higher clustering of cases in the northeast region and a gradual spread toward the southwest over time. The ARIMA model predicted a relatively stable number of new cases each month in 2023 and 2024.
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Affiliation(s)
- Zhi Yun
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Panpan Li
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Jinzhong Wang
- Intensive Care Unit, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Feng Lin
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Wenting Li
- Department of Infectious Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Minhua Weng
- Department of Infectious Diseases, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Yanru Zhang
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Huazhi Wu
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Hui Li
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Xiaofang Cai
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
| | - Xiaobo Li
- Department of Neurosurgery, Haikou Municipal People's Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, China
| | - Xianxian Fu
- Clinical Lab, Haikou Municipal People’s Hospital and Central South University Xiangya Medical College Affiliated Hospital, Haikou, China
| | - Tao Wu
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
- National Health Commission Key Laboratory of Tropical Disease Control, Hainan Medical University, Haikou, China
| | - Yi Gao
- Department of Infectious Diseases, Affiliated Hainan Hospital of Hainan Medical University (Hainan General Hospital), Haikou, China
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Wan Y, Song P, Liu J, Xu X, Lei X. A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM. BMC Infect Dis 2023; 23:879. [PMID: 38102558 PMCID: PMC10722819 DOI: 10.1186/s12879-023-08864-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Hand, foot, and mouth disease (HFMD) is a common infectious disease that poses a serious threat to children all over the world. However, the current prediction models for HFMD still require improvement in accuracy. In this study, we proposed a hybrid model based on autoregressive integrated moving average (ARIMA), ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) to predict the trend of HFMD. METHODS The data used in this study was sourced from the National Clinical Research Center for Child Health and Disorders, Chongqing, China. The daily reported incidence of HFMD from 1 January 2015 to 27 July 2023 was collected to develop an ARIMA-EEMD-LSTM hybrid model. ARIMA, LSTM, ARIMA-LSTM and EEMD-LSTM models were developed to compare with the proposed hybrid model. Root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were adopted to evaluate the performances of the prediction models. RESULTS Overall, ARIMA-EEMD-LSTM model achieved the most accurate prediction for HFMD, with RMSE, MAPE and R2 of 4.37, 2.94 and 0.996, respectively. Performing EEMD on the residual sequence yields 11 intrinsic mode functions. EEMD-LSTM model is the second best, with RMSE, MAPE and R2 of 6.20, 3.98 and 0.996. CONCLUSION Results showed the advantage of ARIMA-EEMD-LSTM model over the ARIMA model, the LSTM model, the ARIMA-LSTM model and the EEMD-LSTM model. For the prevention and control of epidemics, the proposed hybrid model may provide a more powerful help. Compared with other three models, the two integrated with EEMD method showed significant improvement in predictive capability, offering novel insights for modeling of disease time series.
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Affiliation(s)
- Yiran Wan
- School of Public Health, Chongqing Medical University, Chongqing, China
- Research Center for Medicine and Social Development, Chongqing, China
- Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing, China
- Research Center for Public Health Security, Chongqing Medical University, No1 Medical College Rd, Yuzhong District, Chongqing, 400016, People's Republic of China
| | - Ping Song
- Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, No 136. Zhongshan 2Nd Rd, Yuzhong District, Chongqing, 400014, People's Republic of China
| | - Jiangchen Liu
- School of Mathematical Science, Chongqing Normal University, Chongqing, China
| | - Ximing Xu
- Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, No 136. Zhongshan 2Nd Rd, Yuzhong District, Chongqing, 400014, People's Republic of China.
| | - Xun Lei
- School of Public Health, Chongqing Medical University, Chongqing, China.
- Research Center for Medicine and Social Development, Chongqing, China.
- Collaborative Innovation Center of Social Risks Governance in Health, Chongqing Medical University, Chongqing, China.
- Research Center for Public Health Security, Chongqing Medical University, No1 Medical College Rd, Yuzhong District, Chongqing, 400016, People's Republic of China.
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Man H, Huang H, Qin Z, Li Z. Analysis of a SARIMA-XGBoost model for hand, foot, and mouth disease in Xinjiang, China. Epidemiol Infect 2023; 151:e200. [PMID: 38044833 PMCID: PMC10729004 DOI: 10.1017/s0950268823001905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Hand, foot, and mouth disease (HFMD) is a common childhood infectious disease. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. In this paper, we propose a combined SARIMA-XGBoost model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The SARIMA model is used for seasonal trends, and the XGBoost algorithm is applied for the nonlinear effects of meteorological factors. The geographical and temporal weighted regression model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The analysis results show that the HFMD exhibits seasonal characteristics, peaking from May to August each year, and the HFMD incidence has significant spatial heterogeneity. The meteorological factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the proposed SARIMA-XGBoost model is superior to other models in accuracy, especially in regions with a high incidence of HFMD.
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Affiliation(s)
- Haojie Man
- School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China
| | - Hanting Huang
- School of Mathematical Sciences, Beihang University, Beijing, China
| | - Zhuangyan Qin
- College of Mathematics and System Science, Xinjiang University, Urumqi, China
| | - Zhiming Li
- College of Mathematics and System Science, Xinjiang University, Urumqi, China
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6
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Wang Y, Yi X, Luo M, Wang Z, Qin L, Hu X, Wang K. Prediction of outpatients with conjunctivitis in Xinjiang based on LSTM and GRU models. PLoS One 2023; 18:e0290541. [PMID: 37733673 PMCID: PMC10513229 DOI: 10.1371/journal.pone.0290541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 08/10/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Reasonable and accurate forecasting of outpatient visits helps hospital managers optimize the allocation of medical resources, facilitates fine hospital management, and is of great significance in improving hospital efficiency and treatment capacity. METHODS Based on conjunctivitis outpatient data from the First Affiliated Hospital of Xinjiang Medical University Ophthalmology from 2017/1/1 to 2019/12/31, this paper built and evaluated Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for outpatient visits prediction. RESULTS In predicting the number of conjunctivitis visits over the next 31 days, the LSTM model had a root mean square error (RMSE) of 2.86 and a mean absolute error (MAE) of 2.39, the GRU model has an RMSE of 2.60 and an MAE of 1.99. CONCLUSIONS The GRU method can better predict trends in hospital outpatient flow over time, thus providing decision support for medical staff and outpatient management.
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Affiliation(s)
- Yijia Wang
- College of Mathematics and System Science, Xinjiang University, Urumqi Xinjiang, China
| | - Xianglong Yi
- Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Mei Luo
- Department of Ophthalmology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China
| | - Zhe Wang
- Department of Medical Information, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Long Qin
- EClinCloud (Shenzhen) Technology Co., Ltd, Shenzhen Bay Science and Technology Ecological Park, Nanshan District, Shenzhen, Guangdong, China
| | - Xijian Hu
- College of Mathematics and System Science, Xinjiang University, Urumqi Xinjiang, China
| | - Kai Wang
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi Xinjiang, China
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7
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Geng X, Ma Y, Cai W, Zha Y, Zhang T, Zhang H, Yang C, Yin F, Shui T. Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China. PLoS Negl Trop Dis 2023; 17:e0011587. [PMID: 37683009 PMCID: PMC10511093 DOI: 10.1371/journal.pntd.0011587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 09/20/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. METHODS We collected the daily number of HFMD cases among children aged 0-14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. RESULTS From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. CONCLUSIONS The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors.
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Affiliation(s)
- Xiaoran Geng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yue Ma
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Wennian Cai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yuanyi Zha
- Kunming Medical University, Kunming, China
| | - Tao Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Huadong Zhang
- Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Changhong Yang
- Sichuan Center for Disease Control and Prevention, Chengdu, China
| | - Fei Yin
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Tiejun Shui
- Yunnan Center for Disease Control and Prevention, Kunming, 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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Bai L, Lu K, Dong Y, Wang X, Gong Y, Xia Y, Wang X, Chen L, Yan S, Tang Z, Li C. Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model. Sci Rep 2023; 13:2691. [PMID: 36792764 PMCID: PMC9930044 DOI: 10.1038/s41598-023-29897-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.
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Affiliation(s)
- Lu Bai
- grid.263761.70000 0001 0198 0694Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123 China ,grid.263761.70000 0001 0198 0694Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, 215123 China
| | - Ke Lu
- grid.452273.50000 0004 4914 577XDepartment of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, No. 91 West of Qianjin Road, Suzhou, 215300 Jiangsu China
| | - Yongfei Dong
- grid.263761.70000 0001 0198 0694Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123 China ,grid.263761.70000 0001 0198 0694Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, 215123 China
| | - Xichao Wang
- grid.263761.70000 0001 0198 0694Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123 China ,grid.263761.70000 0001 0198 0694Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, 215123 China
| | - Yaqin Gong
- grid.452273.50000 0004 4914 577XInformation Department, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, 215300 Jiangsu China
| | - Yunyu Xia
- Meteorological Bureau of Kunshan City, Suzhou, 215337 Jiangsu China
| | - Xiaochun Wang
- Meteorological Bureau of Kunshan City, Suzhou, 215337 Jiangsu China
| | - Lin Chen
- Ecology and Environment Bureau of Kunshan City, Suzhou, 215330 Jiangsu China
| | - Shanjun Yan
- Ecology and Environment Bureau of Kunshan City, Suzhou, 215330 Jiangsu China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, China. .,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, 215123, China.
| | - Chong Li
- Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, No. 91 West of Qianjin Road, Suzhou, 215300, Jiangsu, China.
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Yang C, An S, Qiao B, Guan P, Huang D, Wu W. Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:20369-20385. [PMID: 36255582 PMCID: PMC9579594 DOI: 10.1007/s11356-022-23643-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.
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Affiliation(s)
- Chuan Yang
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Shuyi An
- Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning China
| | - Baojun Qiao
- Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning China
| | - Peng Guan
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Desheng Huang
- Department of Intelligent Computing, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
| | - Wei Wu
- Department of Mathematics, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning China
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11
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He J, Wang Y, Wei X, Sun H, Xu Y, Yin W, Wang Y, Zhang W. Spatial-temporal dynamics and time series prediction of HFRS in mainland China: A long-term retrospective study. J Med Virol 2023; 95:e28269. [PMID: 36320103 DOI: 10.1002/jmv.28269] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/08/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in mainland China. The current study aims to characterize the spatial-temporal dynamics of HFRS in mainland China during a long-term period (1950-2018). A total of 1 665 431 cases of HFRS were reported with an average annual incidence of 54.22 cases/100 000 individuals during 1950-2018. The joint regression model was used to define the global trend of the HFRS cases with an increasing-decreasing-slightly increasing-decreasing-slightly increasing trend during the 68 years. Then spatial correlation analysis and wavelet cluster analysis were used to identify four types of clusters of HFRS cases located in central and northeastern China. Lastly, the prophet model outperforms auto-regressive integrated moving average model in the HFRS modeling. Our findings will help reduce the knowledge gap on the transmission dynamics and distribution patterns of the HFRS in mainland China and facilitate to take effective preventive and control measures for the high-risk epidemic area.
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Affiliation(s)
- Junyu He
- Ocean College, Zhejiang University, Zhoushan, China.,Ocean Academy, Zhejiang University, Zhoushan, China
| | - Yanding Wang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Xianyu Wei
- Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Hailong Sun
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Yuanyong Xu
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Wenwu Yin
- Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Wenyi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, China Medical University, Shenyang, China.,Chinese PLA Center for Disease Control and Prevention, Beijing, China.,Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China
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12
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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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Jayaraj VJ, Hoe VCW. Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16880. [PMID: 36554768 PMCID: PMC9779090 DOI: 10.3390/ijerph192416880] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/02/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.
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Affiliation(s)
- Vivek Jason Jayaraj
- Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
- Ministry of Health Malaysia, Putrajaya 62000, Malaysia
| | - Victor Chee Wai Hoe
- Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
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Spatiotemporal Distributions of Foot and Mouth Disease Between 2010-2019 in Turkey. ACTA VET-BEOGRAD 2022. [DOI: 10.2478/acve-2022-0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Abstract
Foot and mouth disease (FMD) is one of the most contagious diseases of livestock with a significant economic impact affecting most countries in the world over the years. In Turkey, FMD is endemic, but there have not been national studies conducted to analyze spatiotemporal pattern of FMD yet. This study was carried out to identify the spatial and temporal distribution of FMD outbreaks in Turkey from January 2010 to December 2019, to guide the eradication following development of control programs against the disease. Thematic maps were produced to determine FMD sensitive regions and Box-Jenkins time series approach was used to analyze the temporal pattern of FMD. Between these dates, 6698 outbreaks and 246341 cases were reported in Turkey, FMD was recorded multiple times in 96.3% of the provinces (n = 78), and the average incidence of FMD outbreaks at the provincial level was calculated as 8.27/province year. As result of the spatial pattern of FMD, East and Central Anatolia were determined as the regions where the disease was observed intensely. The time series plot of the data showed a general not very regular trend although there was a downward trend with irregular variations. Although, there was no seasonal effect detected by the decomposition of time series, seasonal peaks in the outbreaks were observed, in the spring (n = 2087, 31.16%). In conclusion, the evaluation of spatial and temporal pattern based on FMD outbreaks that are common in Turkey will contribute to eradication of the disease.
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Chen S, Wang X, Zhao J, Zhang Y, Kan X. Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022. Infect Drug Resist 2022; 15:3503-3512. [PMID: 35813085 PMCID: PMC9268244 DOI: 10.2147/idr.s367528] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/23/2022] [Indexed: 11/23/2022] Open
Abstract
Objective Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources. In this study, we predict the incidence of pulmonary tuberculosis by establishing the autoregressive integrated moving average (ARIMA) model and providing support for pulmonary tuberculosis prevention and control during COVID-19 pandemic. Methods Registered tuberculosis(TB) cases from January 2013 to December 2020 in Anhui province were analysed using traditional descriptive epidemiological methods. Then we used the monthly incidence rate of TB from January 2013 through June 2020 to construct ARIMA model, and used the incidence rate from July 2020 to December 2020 to evaluate the forecasting accuracy. Ljung Box test, Akaike's information criterion(AICc), Bayesian information criterion(BIC) and Realtive error were used to evaluate the model fitting and forecasting effect, Finally, the optimal model was used to forecast the expected monthly incidence of tuberculosis for 2021 and 2022 to learn about the incidence trend. Results A total of 255,656 TB cases were registered. The reported rate of tuberculosis was highest in 2013 and lowest in 2020. The peak incidence was in March, Tongling (71.97/100,000), Chizhou (59.93/100,000), and Huainan (58.36/100,000) had the highest number of cases. The ratio of male to female incidence was 2.59:1, with the largest proportion of people being between 66 and 75 years old. The main occupation of patients was farmer. ARIMA (0, 1, 1) (0, 1, 1)12 model was the optimal model to forecast the incidence trend of TB. Conclusion Tongling, Chizhou, and Huainan should strengthen measures for TB. In particular, the government should pay more attention on elderly people to prevent tuberculosis infections. The rate of TB patient registration and reporting has decreased under the pandemic of COVID-19. The ARIMA model can be a useful tool for predicting future TB cases.
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Affiliation(s)
- Shuangshuang Chen
- Department of Scientific Research and Education, Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute), Hefei, People’s Republic of China
| | - Xinqiang Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People’s Republic of China
| | - Jiawen Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People’s Republic of China
| | - Yongzhong Zhang
- Department of Tuberculosis Prevent and Control, Anhui Provincial Tuberculosis Institute, Hefei, People’s Republic of China
| | - Xiaohong Kan
- Department of Scientific Research and Education, Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute), Hefei, People’s Republic of China
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, People’s Republic of China
- Correspondence: Xiaohong Kan, Department of Scientific Research and Education, Anhui Chest Hospital (Anhui Provincial Tuberculosis Institute), Hefei, 230022, People’s Republic of China, Tel +86 0551-63615340, Email
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Using the SARIMA Model to Forecast the Fourth Global Wave of Cumulative Deaths from COVID-19: Evidence from 12 Hard-Hit Big Countries. ECONOMETRICS 2022. [DOI: 10.3390/econometrics10020018] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented shock to the world’s economy, and it has interrupted the lives and livelihood of millions of people. In the last two years, a large body of literature has attempted to forecast the main dimensions of the COVID-19 outbreak using a wide set of models. In this paper, I forecast the short- to mid-term cumulative deaths from COVID-19 in 12 hard-hit big countries around the world as of 20 August 2021. The data used in the analysis were extracted from the Our World in Data COVID-19 dataset. Both non-seasonal and seasonal autoregressive integrated moving averages (ARIMA and SARIMA) were estimated. The analysis showed that: (i) ARIMA/SARIMA forecasts were sufficiently accurate in both the training and test set by always outperforming the simple alternative forecasting techniques chosen as benchmarks (Mean, Naïve, and Seasonal Naïve); (ii) SARIMA models outperformed ARIMA models in 47 out 48 metrics (in forecasting future values), i.e., on 97.9% of all the considered forecast accuracy measures (mean absolute error [MAE], mean absolute percentage error [MAPE], mean absolute scaled error [MASE], and the root mean squared error [RMSE]), suggesting a clear seasonal pattern in the data; and (iii) the forecasted values from SARIMA models fitted very well the observed (real-time) data for the period 21 August 2021–19 September 2021 for almost all the countries analyzed. This article shows that SARIMA can be safely used for both the short- and medium-term predictions of COVID-19 deaths. Thus, this approach can help government authorities to monitor and manage the huge pressure that COVID-19 is exerting on national healthcare systems.
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Zhao D, Zhang H, Cao Q, Wang Z, He S, Zhou M, Zhang R. The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China. PLoS One 2022; 17:e0262734. [PMID: 35196309 PMCID: PMC8865644 DOI: 10.1371/journal.pone.0262734] [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: 07/30/2021] [Accepted: 01/04/2022] [Indexed: 11/25/2022] Open
Abstract
Background and objective Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. Methods The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. Results There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. Conclusions Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.
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Affiliation(s)
- Daren Zhao
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China
| | - Huiwu Zhang
- Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China
| | - Qing Cao
- Department of Medical Administration, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, Sichuan, P.R. China
| | - Zhiyi Wang
- Department of Medical Administration, Sichuan Cancer Hospital & Institute, Chengdu, Sichuan, P.R. China
| | - Sizhang He
- Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P.R. China
| | - Minghua Zhou
- Department of Medical Administration, Luzhou People’s Hospital, Luzhou, Sichuan, P.R. China
| | - Ruihua Zhang
- School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, P.R. China
- * E-mail:
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Meng D, Xu J, Zhao J. Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost. PLoS One 2021; 16:e0261629. [PMID: 34936688 PMCID: PMC8694472 DOI: 10.1371/journal.pone.0261629] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/06/2021] [Indexed: 12/13/2022] Open
Abstract
Hand, foot and mouth disease (HFMD) is an increasingly serious public health problem, and it has caused an outbreak in China every year since 2008. Predicting the incidence of HFMD and analyzing its influential factors are of great significance to its prevention. Now, machine learning has shown advantages in infectious disease models, but there are few studies on HFMD incidence based on machine learning that cover all the provinces in mainland China. In this study, we proposed two different machine learning algorithms, Random Forest and eXtreme Gradient Boosting (XGBoost), to perform our analysis and prediction. We first used Random Forest to examine the association between HFMD incidence and potential influential factors for 31 provinces in mainland China. Next, we established Random Forest and XGBoost prediction models using meteorological and social factors as the predictors. Finally, we applied our prediction models in four different regions of mainland China and evaluated the performance of them. Our results show that: 1) Meteorological factors and social factors jointly affect the incidence of HFMD in mainland China. Average temperature and population density are the two most significant influential factors; 2) Population flux has different delayed effect in affecting HFMD incidence in different regions. From a national perspective, the model using population flux data delayed for one month has better prediction performance; 3) The prediction capability of XGBoost model was better than that of Random Forest model from the overall perspective. XGBoost model is more suitable for predicting the incidence of HFMD in mainland China.
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Affiliation(s)
- Delin Meng
- Complexity Science Institute, Qingdao University, Qingdao, Shandong, China
| | - Jun Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Jijun Zhao
- Complexity Science Institute, Qingdao University, Qingdao, Shandong, China
- * E-mail:
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Punyapornwithaya V, Jampachaisri K, Klaharn K, Sansamur C. Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models. Front Vet Sci 2021; 8:775114. [PMID: 34917670 PMCID: PMC8669476 DOI: 10.3389/fvets.2021.775114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/05/2021] [Indexed: 12/23/2022] Open
Abstract
Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.
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Affiliation(s)
- Veerasak Punyapornwithaya
- Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, Thailand.,Research Group for Veterinary Public Health, Faculty of Veterinary Medicine Chiang Mai University, Chiang Mai, Thailand
| | - Katechan Jampachaisri
- Department of Mathematics, Faculty of Science, Naresuan University, Phitsanulok, Thailand
| | - Kunnanut Klaharn
- Bureau of Livestock Standards and Certification, Department of Livestock Development, Bangkok, Thailand
| | - Chalutwan Sansamur
- Akkhraratchakumari Veterinary College, Walailak University, Nakhon Si Thammarat, Thailand
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Abstract
As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.
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Cheng X, Hu J, Luo L, Zhao Z, Zhang N, Hannah MN, Rui J, Lin S, Zhu Y, Wang Y, Yang M, Xu J, Liu X, Yang T, Liu W, Li P, Deng B, Li Z, Liu C, Huang J, Peng Z, Bao C, Chen T. Impact of interventions on the incidence of natural focal diseases during the outbreak of COVID-19 in Jiangsu Province, China. Parasit Vectors 2021; 14:483. [PMID: 34538265 PMCID: PMC8449989 DOI: 10.1186/s13071-021-04986-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/31/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND During the period of the coronavirus disease 2019 (COVID-19) outbreak, strong intervention measures, such as lockdown, travel restriction, and suspension of work and production, may have curbed the spread of other infectious diseases, including natural focal diseases. In this study, we aimed to study the impact of COVID-19 prevention and control measures on the reported incidence of natural focal diseases (brucellosis, malaria, hemorrhagic fever with renal syndrome [HFRS], dengue, severe fever with thrombocytopenia syndrome [SFTS], rabies, tsutsugamushi and Japanese encephalitis [JE]). METHODS The data on daily COVID-19 confirmed cases and natural focal disease cases were collected from Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Provincial CDC). We described and compared the difference between the incidence in 2020 and the incidence in 2015-2019 in four aspects: trend in reported incidence, age, sex, and urban and rural distribution. An autoregressive integrated moving average (ARIMA) (p, d, q) × (P, D, Q)s model was adopted for natural focal diseases, malaria and severe fever with thrombocytopenia syndrome (SFTS), and an ARIMA (p, d, q) model was adopted for dengue. Nonparametric tests were used to compare the reported and the predicted incidence in 2020, the incidence in 2020 and the previous 4 years, and the difference between the duration from illness onset date to diagnosed date (DID) in 2020 and in the previous 4 years. The determination coefficient (R2) was used to evaluate the goodness of fit of the model simulation. RESULTS Natural focal diseases in Jiangsu Province showed a long-term seasonal trend. The reported incidence of natural focal diseases, malaria and dengue in 2020 was lower than the predicted incidence, and the difference was statistically significant (P < 0.05). The reported incidence of brucellosis in July, August, October and November 2020, and SFTS in May to November 2020 was higher than that in the same period in the previous 4 years (P < 0.05). The reported incidence of malaria in April to December 2020, HFRS in March, May and December 2020, and dengue in July to November 2020 was lower than that in the same period in the previous 4 years (P < 0.05). In males, the reported incidence of malaria in 2020 was lower than that in the previous 4 years, and the reported incidence of dengue in 2020 was lower than that in 2017-2019. The reported incidence of malaria in the 20-60-year age group was lower than that in the previous 4 years; the reported incidence of dengue in the 40-60-year age group was lower than that in 2016-2018. The reported cases of malaria in both urban and rural areas were lower than in the previous 4 years. The DID of brucellosis and SFTS in 2020 was shorter than that in 2015-2018; the DID of tsutsugamushi in 2020 was shorter than that in the previous 4 years. CONCLUSIONS Interventions for COVID-19 may help control the epidemics of natural focal diseases in Jiangsu Province. The reported incidence of natural focal diseases, especially malaria and dengue, decreased during the outbreak of COVID-19 in 2020. COVID-19 prevention and control measures had the greatest impact on the reported incidence of natural focal diseases in males and people in the 20-60-year age group.
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Affiliation(s)
- Xiaoqing Cheng
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | - Jianli Hu
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Nan Zhang
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China
| | | | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Shengnan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Yuanzhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Jingwen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China
| | - Zhihang Peng
- School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
| | - Changjun Bao
- Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu Institution of Public Health), Nanjing, 210009, Jiangsu, People's Republic of China.
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, 361102, Fujian, People's Republic of China.
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Alabdulrazzaq H, Alenezi MN, Rawajfih Y, Alghannam BA, Al-Hassan AA, Al-Anzi FS. On the accuracy of ARIMA based prediction of COVID-19 spread. RESULTS IN PHYSICS 2021; 27:104509. [PMID: 34307005 PMCID: PMC8279942 DOI: 10.1016/j.rinp.2021.104509] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 06/25/2021] [Accepted: 06/27/2021] [Indexed: 05/27/2023]
Abstract
COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible-Infected-Recovered model, Susceptible-Exposed-Infected-Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait's gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson's correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.
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Affiliation(s)
- Haneen Alabdulrazzaq
- Computer Science & Information Systems Department, Public Authority for Applied Education & Training, Kuwait
| | - Mohammed N Alenezi
- Computer Science & Information Systems Department, Public Authority for Applied Education & Training, Kuwait
| | | | - Bareeq A Alghannam
- Computer Science & Information Systems Department, Public Authority for Applied Education & Training, Kuwait
| | - Abeer A Al-Hassan
- Information Systems and Operations Management Department, Kuwait University, Kuwait
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Yu C, Xu C, Li Y, Yao S, Bai Y, Li J, Wang L, Wu W, Wang Y. Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model. Infect Drug Resist 2021; 14:2809-2821. [PMID: 34321897 PMCID: PMC8312251 DOI: 10.2147/idr.s304652] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 04/26/2021] [Indexed: 12/11/2022] Open
Abstract
Objective The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China. Methods Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA) method. Results Following the modelling procedures of the SARIMA and TBATS methods, the SARIMA (1,0,1)(0,1,1)12 and TBATS (0.024, {1,1}, 0.855, {<12,4>}) specifications were recognized as being the optimal models, respectively, for the 12-step ahead forecasting, along with the SARIMA (1,0,1)(0,1,1)12 and TBATS (0.062, {1,3}, 0.86, {<12,4>}) models as being the optimal models, respectively, for the 24-step ahead forecasting. Among them, the optimal TBATS models produced lower error rates in both 12-step and 24-step ahead forecasting aspects compared to the preferred SARIMA models. Descriptive analysis of the data showed a significantly high level and a marked dual seasonal pattern in the HFMD morbidity. Conclusion The TBATS model has the capacity to outperform the most frequently used SARIMA model in forecasting the HFMD incidence in China, and it can be recommended as a flexible and useful tool in the decision-making process of HFMD prevention and control in China.
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Affiliation(s)
- Chongchong Yu
- 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, 100069, People's Republic of China
| | - Yuhong Li
- National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, 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
| | - Yichun Bai
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Jizhen 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
| | - Weidong Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People's Republic of China
| | - Yongbin Wang
- 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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Zhang R, Guo Z, Meng Y, Wang S, Li S, Niu R, Wang Y, Guo Q, Li Y. Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18116174. [PMID: 34200378 PMCID: PMC8201362 DOI: 10.3390/ijerph18116174] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/26/2021] [Accepted: 06/03/2021] [Indexed: 11/30/2022]
Abstract
Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.
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Affiliation(s)
- Rui Zhang
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (R.Z.); (Y.M.); (S.W.); (S.L.)
| | - Zhen Guo
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China;
| | - Yujie Meng
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (R.Z.); (Y.M.); (S.W.); (S.L.)
| | - Songwang Wang
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (R.Z.); (Y.M.); (S.W.); (S.L.)
| | - Shaoqiong Li
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (R.Z.); (Y.M.); (S.W.); (S.L.)
| | - Ran Niu
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China;
| | - Yu Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China;
| | - Qing Guo
- Chinese Center for Disease Control and Prevention, Beijing 102206, China; (R.Z.); (Y.M.); (S.W.); (S.L.)
- Correspondence: (Q.G.); (Y.L.); Tel.: +86-10-5890-0410 (Q.G.); Fax: +86-10-5890-0445 (Q.G.)
| | - Yonghong Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China;
- Correspondence: (Q.G.); (Y.L.); Tel.: +86-10-5890-0410 (Q.G.); Fax: +86-10-5890-0445 (Q.G.)
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Guleryuz D. Forecasting outbreak of COVID-19 in Turkey; Comparison of Box-Jenkins, Brown's exponential smoothing and long short-term memory models. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2021; 149:927-935. [PMID: 33776248 PMCID: PMC7983456 DOI: 10.1016/j.psep.2021.03.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 03/15/2021] [Indexed: 05/27/2023]
Abstract
The new coronavirus disease (COVID-19), which first appeared in China in December 2019, has pervaded throughout the world. Because the epidemic started later in Turkey than other European countries, it has the least number of deaths according to the current data. Outbreak management in COVID-19 is of great importance for public safety and public health. For this reason, prediction models can decide the precautionary warning to control the spread of the disease. Therefore, this study aims to develop a forecasting model, considering statistical data for Turkey. Box-Jenkins Methods (ARIMA), Brown's Exponential Smoothing model and RNN-LSTM are employed. ARIMA was selected with the lowest AIC values (12.0342, -2.51411, 12.0253, 3.67729, -4.24405, and 3.66077) as the best fit for the number of total case, the growth rate of total cases, the number of new cases, the number of total death, the growth rate of total deaths and the number of new deaths, respectively. The forecast values of the number of each indicator are stable over time. In the near future, it will not show an increasing trend in the number of cases for Turkey. In addition, the pandemic will become a steady state and an increase in mortality rates will not be expected between 17-31 May. ARIMA models can be used in fresh outbreak situations to ensure health and safety. It is vital to make quick and accurate decisions on the precautions for epidemic preparedness and management, so corrective and preventive actions can be updated considering obtained values.
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Affiliation(s)
- Didem Guleryuz
- Department of Industrial Engineering, Bayburt University, Bayburt, Turkey
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26
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Yu G, Feng H, Feng S, Zhao J, Xu J. Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model. PLoS One 2021; 16:e0246673. [PMID: 33544752 PMCID: PMC7864434 DOI: 10.1371/journal.pone.0246673] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/23/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. MATERIALS AND METHODS We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)-neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA-NNAR hybrid model were established for comparison and estimation. RESULTS The wavelet-based SARIMA-NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. CONCLUSIONS The wavelet-based SARIMA-NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.
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Affiliation(s)
- Gongchao Yu
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Huifen Feng
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
- * E-mail:
| | - Shuang Feng
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Jing Zhao
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
| | - Jing Xu
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China
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27
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Zhu G, Li L, Zheng Y, Zhang X, Zou H. Forecasting Influenza Based on Autoregressive Moving Average and Holt-Winters Exponential Smoothing Models. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021. [DOI: 10.20965/jaciii.2021.p0138] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Influenza outbreaks can be effectively prevented if further outbreaks are predicted as early as possible. This article proposes an autoregressive integrated moving average (ARIMA) model and a Holt-Winters exponential smoothing (HWES) model to analyze tweet data for predicting influenza outbreaks and to visualize the number of flu-infection-related tweets with heat maps. First, textual influenza data for Australia from June 2015 to June 2017 are collected through the Twitter Application Programming Interface (API). Next, the ARIMA and HWES models are applied to predict the difference between the flu tweets and confirmations from the Centers for Disease Control and Prevention. Finally, a visualized heat map based on influenza topics validates the modeling analysis in two different time zones. The results show that the average relative error of the ARIMA (HWES) model is 7.25% (11.29%) for the one-week flu forecast.
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28
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Zheng Y, Wang K, Zhang L, Wang L. Study on the relationship between the incidence of influenza and climate indicators and the prediction of influenza incidence. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:473-481. [PMID: 32815008 DOI: 10.1007/s11356-020-10523-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 08/13/2020] [Indexed: 06/11/2023]
Abstract
In recent 2 years, the incidence of influenza showed a slight upward trend in Guangxi; therefore, some joint actions should be done to help preventing and controlling this disease. The factors analysis of affecting influenza and early prediction of influenza incidence may help policy-making so as to take effective measures to prevent and control influenza. In this study, we used the cross correlation function (CCF) to analyze the effect of climate indicators on influenza incidence, ARIMA and ARIMAX (autoregressive integrated moving average model with exogenous input variables) model methods to do predictive analysis of influenza incidence. The results of CCF analysis showed that climate indicators (PM2.5, PM10, SO2, CO, NO2, O3, average temperature, maximum temperature, minimum temperature, average relative humidity, and sunshine duration) had significant effects on the incidence of influenza. People need to take good precautions in the days of severe air pollution and keep warm in cold weather to prevent influenza. We found that the ARIMAX (1,0,1)(0,0,1)12 with NO2 model has good predictive performance, which can be used to predict the influenza incidence in Guangxi, and the predicted incidence may be useful in developing early warning systems and providing important evidence for influenza control policy-making and public health intervention.
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Affiliation(s)
- Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China.
| | - Kai Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China
| | - Lei Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830011, People's Republic of China
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Li ZQ, Pan HQ, Liu Q, Song H, Wang JM. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infect Dis Poverty 2020; 9:151. [PMID: 33148337 PMCID: PMC7641658 DOI: 10.1186/s40249-020-00771-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 10/21/2020] [Indexed: 12/13/2022] Open
Abstract
Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
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Affiliation(s)
- Zhong-Qi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Hong-Qiu Pan
- Department of Tuberculosis, The Third Hospital of Zhenjiang City, Zhenjiang, 212005, China
| | - Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Huan Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China
| | - Jian-Ming Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave., Nanjing, 211166, China.
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Gao J, Li J, Wang M. Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models. PLoS One 2020; 15:e0241217. [PMID: 33112899 PMCID: PMC7592733 DOI: 10.1371/journal.pone.0241217] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 10/09/2020] [Indexed: 11/18/2022] Open
Abstract
Typhoid and paratyphoid fevers are common enteric diseases causing disability and death in China. Incidence data of typhoid and paratyphoid between 2004 and 2016 in China were analyzed descriptively to explore the epidemiological features such as age-specific and geographical distribution. Cumulative incidence of both fevers displayed significant decrease nationally, displaying a drop of 73.9% for typhoid and 86.6% for paratyphoid in 2016 compared to 2004. Cumulative incidence fell in all age subgroups and the 0–4 years-old children were the most susceptible ones in recent years. A cluster of three southwestern provinces (Yunnan, Guizhou, and Guangxi) were the top high-incidence regions. Grey model GM (1,1) and seasonal autoregressive integrated moving average (SARIMA) model were employed to extract the long-term trends of the diseases. Annual cumulative incidence for typhoid and paratyphoid were formulated by GM (1,1) as x^(t)=−14.98(e−0.10(t−2004)−e−0.10(t−2005)) and x^(t)=−4.96(e−0.19(t−2004)−e−0.19(t−2005)) respectively. SARIMA (0,1,7) × (1,0,1)12 was selected among a collection of constructed models for high R2 and low errors. The predictive models for both fevers forecasted cumulative incidence to continue the slightly downward trend and maintain the cyclical seasonality in near future years. Such data-driven insights are informative and actionable for the prevention and control of typhoid and paratyphoid fevers as serious infectious diseases.
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Affiliation(s)
- Jiaqi Gao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, P. R. China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, P. R. China
| | - Mengqiao Wang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, P. R. China
- * E-mail:
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Suntronwong N, Vichaiwattana P, Klinfueng S, Korkong S, Thongmee T, Vongpunsawad S, Poovorawan Y. Climate factors influence seasonal influenza activity in Bangkok, Thailand. PLoS One 2020; 15:e0239729. [PMID: 32991630 PMCID: PMC7523966 DOI: 10.1371/journal.pone.0239729] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 09/12/2020] [Indexed: 12/05/2022] Open
Abstract
Yearly increase in influenza activity is associated with cold and dry winter in the temperate regions, while influenza patterns in tropical countries vary significantly by regional climates and geographic locations. To examine the association between influenza activity in Thailand and local climate factors including temperature, relative humidity, and rainfall, we analyzed the influenza surveillance data from January 2010 to December 2018 obtained from a large private hospital in Bangkok. We found that approximately one in five influenza-like illness samples (21.6% or 6,678/30,852) tested positive for influenza virus. Influenza virus typing showed that 34.2% were influenza A(H1N1)pdm09, 46.0% were influenza A(H3N2), and 19.8% were influenza B virus. There were two seasonal waves of increased influenza activity. Peak influenza A(H1N1)pdm09 activity occurred in February and again in August, while influenza A(H3N2) and influenza B viruses were primarily detected in August and September. Time series analysis suggests that increased relative humidity was significantly associated with increased influenza activity in Bangkok. Months with peak influenza activity generally followed the most humid months of the year. We performed the seasonal autoregressive integrated moving average (SARIMA) multivariate analysis of all influenza activity on the 2011 to 2017 data to predict the influenza activity for 2018. The resulting model closely resembled the actual observed overall influenza detected that year. Consequently, the ability to predict seasonal pattern of influenza in a large tropical city such as Bangkok may enable better public health planning and underscores the importance of annual influenza vaccination prior to the rainy season.
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Affiliation(s)
- Nungruthai Suntronwong
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Preeyaporn Vichaiwattana
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sirapa Klinfueng
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sumeth Korkong
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Thanunrat Thongmee
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Sompong Vongpunsawad
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yong Poovorawan
- Center of Excellence in Clinical Virology, Department of Pediatrics, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
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Torgerson T, Roberts W, Lester D, Khojasteh J, Vassar M. Public interest in Cannabis during election season: a Google Trends analysis. J Cannabis Res 2020; 2:31. [PMID: 33526135 PMCID: PMC7819328 DOI: 10.1186/s42238-020-00039-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 09/11/2020] [Indexed: 11/10/2022] Open
Abstract
Introduction Given that 72% of internet users seek out health information using an internet search engine (Google being the most popular); we sought to investigate the public internet search interest in cannabis as a health topic when cannabis legislation appeared on state ballots and during presidential elections. Materials and methods We searched Google Trends for “cannabis” as a health topic. Google Trends data were extracted during the time period of May 1, 2008 to May 1, 2019 for the United States (US) and select states (18) within the US including: Alaska, Arizona, Arkansas, California, Colorado, Florida, Maine, Massachusetts, Michigan, Missouri, Nevada, North Dakota, Ohio, Oregon, Oklahoma, South Dakota, Utah, and Washington when cannabis was on the ballot. These state elections were referenda, not legislative votes. We then compared the internet search interest for cannabis before and after each election. To evaluate whether any associations with changes in the volume of cannabis internet searches were specific to the cannabis topic, or also occurred with other topics of general interest during an election year, the authors ran additional analyses of previously popular debated policies during Presidential Elections that may act as control topics. These policies included Education, Gun Control, Climate Change, Global Warming, and Abortion. We used the autoregressive integrated moving average (ARIMA) algorithm to forecast expected relative internet search interests for the 2012 and 2016 Presidential Elections. Individual variables were compared using a linear regression analysis for the beta coefficients performed in Stata Version 15.1 (StataCorp). Results Public internet search interest for “cannabis” increased during the voting month above the previous mean internet search interest for all 18 bills. For the US, observed internet search interest during each Presidential Election was 26.9% [95% CI, 18.4–35.4%] greater than expected in 2012 and 29.8% [95% CI, 20.8–38.8%] greater than expected in 2016. In 2016, significant state-level findings included an increase in relative internet search rates for cannabis in states with higher usage rates of cannabis in the past month (Coeff (95% CI), 3.4 (2.8–4.0)) and past month illicit drug use except cannabis rates (Coeff (95% CI), 17.4 (9.8–25.0)). Relative internet search rates for cannabis from 2008 to 2019 were also associated with increased cannabis usage in the past month (Coeff (95% CI), 3.1 (2.5–3.7)). States with higher access to legal cannabis were associated with higher relative internet search volumes for cannabis (Coeff (95% CI), 0.31 (0.15–0.46)). Of the five additional policies that were searched as topics, only two showed an increase in internet search interest during each Presidential Election. Climate Change increased by 3.5% [95% CI, − 13-20%] in 2012 and 20.1% [95% CI, 0–40%] in 2016 while Global Warming increased by 1.1% [95% CI, − 19-21%] in 2012 and 4.6% [95% CI, − 6-15%] in 2016. Conclusion Based on these results, we expect public interest in cannabis will spike prior to the Presidential election in 2020. Of the five selected control policies, only two showed an increase in internet search interest during both Presidential Elections and neither exceeded the internet search increase of cannabis. These results may indicate the growing awareness of cannabis in the US and mark a possible target for the timely dissemination of evidence-based information regarding cannabis and its usage/side-effects during future elections. Consequently, the results of this study may be important to physicians since they will likely receive an increased volume of questions relating to cannabis and its therapeutic uses during election season from interested patients. We recommend establishing a cannabis repository of evidence-based information, providing physician education, and a dosing guide be created to enable physicians to provide high quality care around the issue of cannabis.
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Affiliation(s)
- Trevor Torgerson
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 West 17th Street, Tulsa, OK, 74107, USA.
| | - Will Roberts
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 West 17th Street, Tulsa, OK, 74107, USA
| | - Drew Lester
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 West 17th Street, Tulsa, OK, 74107, USA
| | - Jam Khojasteh
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 West 17th Street, Tulsa, OK, 74107, USA
| | - Matt Vassar
- Office of Medical Student Research, Oklahoma State University Center for Health Sciences, 1111 West 17th Street, Tulsa, OK, 74107, USA
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Shi F, Yu C, Yang L, Li F, Lun J, Gao W, Xu Y, Xiao Y, Shankara SB, Zheng Q, Zhang B, Wang S. Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models. Infect Drug Resist 2020; 13:2465-2475. [PMID: 32801786 PMCID: PMC7383097 DOI: 10.2147/idr.s250038] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 07/05/2020] [Indexed: 01/18/2023] Open
Abstract
Objective The purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS. Methods Monthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period. Results Statistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)12 model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS. Conclusion This study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.
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Affiliation(s)
- Fuyan Shi
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Changlan Yu
- Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People's Republic of China
| | - Liping Yang
- Health and Medical Center, Xijing Hospital, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China
| | - Fangyou Li
- Anqiu City Center for Disease Control and Prevention, Anqiu, Shandong, People's Republic of China
| | - Jiangtao Lun
- Anqiu Meteorological Bureau, Anqiu, Shandong, People's Republic of China
| | - Wenfeng Gao
- Department of Immunology and Rheumatology, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Yongyong Xu
- Department of Health Statistics, School of Military Preventive Medicine, Air Force Military Medical University, Xi'an, Shannxi, People's Republic of China
| | - Yufei Xiao
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Sravya B Shankara
- Program in Health: Science, Society, and Policy, Brandeis University, Waltham, MA, USA
| | - Qingfeng Zheng
- Institute for Hospital Management of Tsinghua University, Tsinghua Campus, Shenzhen, People's Republic of China
| | - Bo Zhang
- Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Suzhen Wang
- Department of Health Statistics, School of Public Health and Management, Weifang Medical University, Weifang, Shandong, People's Republic of China
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Zha WT, Li WT, Zhou N, Zhu JJ, Feng R, Li T, Du YB, Liu Y, Hong XQ, Lv Y. Effects of meteorological factors on the incidence of mumps and models for prediction, China. BMC Infect Dis 2020; 20:468. [PMID: 32615923 PMCID: PMC7331163 DOI: 10.1186/s12879-020-05180-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 06/19/2020] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Mumps is an acute respiratory infectious disease with obvious regional and seasonal differences. Exploring the impact of climate factors on the incidence of mumps and predicting its incidence trend on this basis could effectively control the outbreak and epidemic of mumps. METHODS Considering the great differences of climate in the vast territory of China, this study divided the Chinese mainland into seven regions according to the administrative planning criteria, data of Mumps were collected from the China Disease Prevention and Control Information System, ARIMA model and ARIMAX model with meteorological factors were established to predict the incidence of mumps. RESULTS In this study, we found that precipitation, air pressure, temperature, and wind speed had an impact on the incidence of mumps in most regions of China and the incidence of mumps in the north and southwest China was more susceptible to climate factors. Considering meteorological factors, the average relative error of ARIMAX model was 10.87%, which was lower than ARIMA model (15.57%). CONCLUSIONS Meteorology factors were the important factors which can affect the incidence of mumps, ARIMAX model with meteorological factors could better simulate and predict the incidence of mumps in China, which has certain reference value for the prevention and control of mumps.
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Affiliation(s)
- Wen-Ting Zha
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Wei-Tong Li
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Nan Zhou
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Jia-Jia Zhu
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Ruihua Feng
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Tong Li
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Yan-Bing Du
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Ying Liu
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Xiu-Qin Hong
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081
| | - Yuan Lv
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China, 410081.
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Liu K, Li T, Vongpradith A, Wang F, Peng Y, Wang W, Chai C, Chen S, Zhang Y, Zhou L, Chen X, Bian Q, Chen B, Wang X, Jiang J. Identification and Prediction of Tuberculosis in Eastern China: Analyses from 10-year Population-based Notification Data in Zhejiang Province, China. Sci Rep 2020; 10:7425. [PMID: 32367050 PMCID: PMC7198485 DOI: 10.1038/s41598-020-64387-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 04/13/2020] [Indexed: 11/29/2022] Open
Abstract
Tuberculosis, a severe infectious disease caused by the Mycobacterium tuberculosis, arouses huge concerns globally. In this study, a total of 331,594 TB cases in Zhejiang Province were notified during the period of 2009-2018 with the gender ratio of male to female 2.16:1. The notified TB incidences demonstrated a continuously declining trend from 75.38/100,000 to 52.25/100,000. Seasonally, the notified TB cases presented as low in January and February closely followed an apparent rise in March and April. Further stratification analysis by both genders demonstrated the double peak phenomenon in the younger population ("15-35") and the elders (">55") of the whole group. Results from the rate difference (RD) analysis showed that the rising TB incidence mainly presented in the young group of "15-20" and elder group of "65-70", implying that some implementations such as the increased frequency of checkup in specific student groups and strengthening of elder health examination could be explored and integrated into available health policy. Finally, the SARIMA (2,0,2) (0,1,1)12 was determined as the optimal prediction model, which could be used in the further prediction of TB in Zhejiang Province.
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Affiliation(s)
- Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Tao Li
- Chinese Center for Disease Control and Prevention, Beijing, People's Republic of China
| | - Avina Vongpradith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Fei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Ying Peng
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Chengliang Chai
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Lin Zhou
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Xinyi Chen
- Ningbo University, Ningbo, Zhejiang Province, People's Republic of China
| | - Qiao Bian
- Ningbo University, Ningbo, Zhejiang Province, People's Republic of China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
| | - Xiaomeng Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
| | - Jianmin Jiang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
- Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
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Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018. Epidemiol Infect 2020; 147:e82. [PMID: 30868999 PMCID: PMC6518604 DOI: 10.1017/s095026881800362x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Seasonal autoregressive-integrated moving average (SARIMA) has been widely used to model and forecast incidence of infectious diseases in time-series analysis. This study aimed to model and forecast monthly cases of hand, foot and mouth disease (HFMD) in China. Monthly incidence HFMD cases in China from May 2008 to August 2018 were analysed with the SARIMA model. A seasonal variation of HFMD incidence was found from May 2008 to August 2018 in China, with a predominant peak from April to July and a trough from January to March. In addition, the annual peak occurred periodically with a large annual peak followed by a relatively small annual peak. A SARIMA model of SARIMA (1, 1, 2) (0, 1, 1)12 was identified, and the mean error rate and determination coefficient were 16.86% and 94.27%, respectively. There was an annual periodicity and seasonal variation of HFMD incidence in China, which could be predicted well by a SARIMA (1, 1, 2) (0, 1, 1)12 model.
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Cao LT, Liu HH, Li J, Yin XD, Duan Y, Wang J. Relationship of meteorological factors and human brucellosis in Hebei province, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 703:135491. [PMID: 31740063 DOI: 10.1016/j.scitotenv.2019.135491] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 10/31/2019] [Accepted: 11/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Brucellosis has always been one of the major public health problems in China. Investigating the influencing factors of brucellosis is conducive to its prevention and control. The incidence trend of brucellosis shows an obvious seasonality, suggesting that there may be a correlation between brucellosis and meteorological factors, but related studies were few. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological factors and brucellosis. METHODS The data of monthly incidence of brucellosis and meteorological factors in Hebei province from January 2004 to December 2015 were collected from the Chinese Public Health Science Data Center and Chinese meteorological data website. An ARIMA model incorporated with covariables was conducted to estimate the effects of meteorological variables on brucellosis. RESULTS There was a highest peak from May to July every year and an upward trend during the study period. Atmospheric pressure, wind speed, mean temperature, and relative humidity had significant effects on brucellosis. The ARIMA(1,0,0)(1,1,0)12 model with the covariates of atmospheric pressure, wind speed and mean temperature was the optimal model. The results showed that the atmospheric pressure with a 2-month lag (β = -0.004, p = 0.037), the wind speed with a 1-month lag (β = 0.030, p = 0.035), and the mean temperature with a 2-month lag (β = -0.003, p = 0.034) were significant predictors. CONCLUSION Our study suggests that atmospheric pressure, wind speed, mean temperature, and relative humidity have a significant impact on brucellosis. Further understanding of its mechanism would help facilitate the monitoring and early warning of brucellosis.
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Affiliation(s)
- Long-Ting Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230036, China
| | - Hong-Hui Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230036, China
| | - Juan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230036, China
| | - Xiao-Dong Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230036, China
| | - Yu Duan
- Division of Life Sciences and Medicine, The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230036, China
| | - Jing Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui 230036, China.
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Nakamura GM, Cardoso GC, Martinez AS. Improved susceptible-infectious-susceptible epidemic equations based on uncertainties and autocorrelation functions. ROYAL SOCIETY OPEN SCIENCE 2020; 7:191504. [PMID: 32257317 PMCID: PMC7062106 DOI: 10.1098/rsos.191504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 01/27/2020] [Indexed: 06/01/2023]
Abstract
Compartmental equations are primary tools in the study of disease spreading processes. They provide accurate predictions for large populations but poor results whenever the integer nature of the number of agents is evident. In the latter instance, uncertainties are relevant factors for pathogen transmission. Starting from the agent-based approach, we investigate the role of uncertainties and autocorrelation functions in the susceptible-infectious-susceptible (SIS) epidemic model, including their relationship with epidemiological variables. We find new differential equations that take uncertainties into account. The findings provide improved equations, offering new insights on disease spreading processes.
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Affiliation(s)
- Gilberto M. Nakamura
- Université Paris-Saclay, CNRS/IN2P3, and Université de Paris, IJCLab, 91405 Orsay, France
- Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo (USP), Ribeirão Preto 14040-901, Brazil
- Instituto Nacional de Ciência e Tecnologia – Sistemas Complexos (INCT-SC), Rio de Janeiro, Brazil
| | - George C. Cardoso
- Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo (USP), Ribeirão Preto 14040-901, Brazil
| | - Alexandre S. Martinez
- Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo (USP), Ribeirão Preto 14040-901, Brazil
- Instituto Nacional de Ciência e Tecnologia – Sistemas Complexos (INCT-SC), Rio de Janeiro, Brazil
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Liu W, Bao C, Zhou Y, Ji H, Wu Y, Shi Y, Shen W, Bao J, Li J, Hu J, Huo X. Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China. BMC Infect Dis 2019; 19:828. [PMID: 31590636 PMCID: PMC6781406 DOI: 10.1186/s12879-019-4457-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 09/10/2019] [Indexed: 08/22/2023] Open
Abstract
Background Hand, foot and mouth disease (HFMD) is a rising public health problem and has attracted considerable attention worldwide. The purpose of this study was to develop an optimal model with meteorological factors to predict the epidemic of HFMD. Methods Two types of methods, back propagation neural networks (BP) and auto-regressive integrated moving average (ARIMA), were employed to develop forecasting models, based on the monthly HFMD incidences and meteorological factors during 2009–2016 in Jiangsu province, China. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed to select model and evaluate the performance of the models. Results Four models were constructed. The multivariate BP model was constructed using the HFMD incidences lagged from 1 to 4 months, mean temperature, rainfall and their one order lagged terms as inputs. The other BP model was fitted just using the lagged HFMD incidences as inputs. The univariate ARIMA model was specified as ARIMA (1,0,1)(1,1,0)12 (AIC = 1132.12, BIC = 1440.43). And the multivariate ARIMAX with one order lagged temperature as external predictor was fitted based on this ARIMA model (AIC = 1132.37, BIC = 1142.76). The multivariate BP model performed the best in both model fitting stage and prospective forecasting stage, with a MAPE no more than 20%. The performance of the multivariate ARIMAX model was similar to that of the univariate ARIMA model. Both performed much worse than the two BP models, with a high MAPE near to 40%. Conclusion The multivariate BP model effectively integrated the autocorrelation of the HFMD incidence series. Meanwhile, it also comprehensively combined the climatic variables and their hysteresis effects. The introduction of the climate terms significantly improved the prediction accuracy of the BP model. This model could be an ideal method to predict the epidemic level of HFMD, which is of great importance for the public health authorities.
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Affiliation(s)
- Wendong Liu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China.
| | - Changjun Bao
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Yuping Zhou
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Hong Ji
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Ying Wu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Yingying Shi
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Wenqi Shen
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Jing Bao
- Jiangsu Meteorological Service Center, Nanjing, China
| | - Juan Li
- Jiangsu Meteorological Service Center, Nanjing, China
| | - Jianli Hu
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
| | - Xiang Huo
- Jiangsu Province Center for Diseases Control and Prevention, Nanjing, China
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Liu Q, Li Z, Ji Y, Martinez L, Zia UH, Javaid A, Lu W, Wang J. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infect Drug Resist 2019; 12:2311-2322. [PMID: 31440067 PMCID: PMC6666376 DOI: 10.2147/idr.s207809] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 07/06/2019] [Indexed: 01/26/2023] Open
Abstract
Objective Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China. Methods We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect. Results During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)12 and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)12 model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model. Conclusion Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.
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Affiliation(s)
- Qiao Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.,Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, People's Republic of China
| | - Zhongqi Li
- 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
| | - Leonardo Martinez
- Division of Infectious Diseases and Geographic Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Ui Haq Zia
- Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Arshad Javaid
- Faculty of Public Health and Social Sciences, Khyber Medical University, Peshawar, Pakistan
| | - Wei Lu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, 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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Wang Y, Xu C, Zhang S, Yang L, Wang Z, Zhu Y, Yuan J. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep 2019; 9:8046. [PMID: 31142826 PMCID: PMC6541597 DOI: 10.1038/s41598-019-44469-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 03/06/2019] [Indexed: 02/03/2023] Open
Abstract
The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.
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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, Hebei Province, P.R. China
| | - Chunjie Xu
- Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, P.R. China
| | - Shengkui Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Li Yang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Zhende Wang
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Ying Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. China
| | - Juxiang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, P.R. 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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Song C, Shi X, Bo Y, Wang J, Wang Y, Huang D. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian Spatiotemporally Varying Coefficients (STVC) model in Sichuan, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 648:550-560. [PMID: 30121533 DOI: 10.1016/j.scitotenv.2018.08.114] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 08/01/2018] [Accepted: 08/08/2018] [Indexed: 05/05/2023]
Abstract
BACKGROUND Pediatric hand, foot, and mouth disease (HFMD) has generally been found to be associated with climate. However, knowledge about how this association varies spatiotemporally is very limited, especially when considering the influence of local socioeconomic conditions. This study aims to identify multi-sourced HFMD environmental factors and further quantify the spatiotemporal nonstationary effects of various climate factors on HFMD occurrence. METHODS We propose an innovative method, named spatiotemporally varying coefficients (STVC) model, under the Bayesian hierarchical modeling framework, for exploring both spatial and temporal nonstationary effects in climate covariates, after controlling for socioeconomic effects. We use data of monthly county-level HFMD occurrence and data of related climate and socioeconomic variables in Sichuan, China from 2009 to 2011 for our experiments. RESULTS Cross-validation experiments showed that the STVC model achieved the best average prediction accuracy (81.98%), compared with ordinary (68.27%), temporal (72.34%), spatial (75.99%) and spatiotemporal (77.60%) ecological models. The STVC model also outperformed these models in the Bayesian model evaluation. In this study, the STVC model was able to spatialize the risk indicator odds ratio (OR) into local ORs to represent spatial and temporal varying disease-climate relationships. We detected local temporal nonlinear seasonal trends and spatial hot spots for both disease occurrence and disease-climate associations over 36 months in Sichuan, China. Among the six representative climate variables, temperature (OR = 2.59), relative humidity (OR = 1.35), and wind speed (OR = 0.65) were not only overall related to the increase of HFMD occurrence, but also demonstrated spatiotemporal variations in their local associations with HFMD. CONCLUSION Our findings show that county-level HFMD interventions may need to consider varying local-scale spatial and temporal disease-climate relationships. Our proposed Bayesian STVC model can capture spatiotemporal nonstationary exposure-response relationships for detailed exposure assessments and advanced risk mapping, and offers new insights to broader environmental science and spatial statistics.
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Affiliation(s)
- Chao Song
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu, Sichuan 610500, China; Department of Geography, Dartmouth College, Hanover, NH 03755, USA; State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xun Shi
- Department of Geography, Dartmouth College, Hanover, NH 03755, USA.
| | - Yanchen Bo
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Jinfeng Wang
- State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Wang
- State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Dacang Huang
- State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Zhao Y, Ge L, Zhou Y, Sun Z, Zheng E, Wang X, Huang Y, Cheng H. A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS). PLoS One 2018; 13:e0207518. [PMID: 30475830 PMCID: PMC6261020 DOI: 10.1371/journal.pone.0207518] [Citation(s) in RCA: 10] [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: 03/31/2018] [Accepted: 11/01/2018] [Indexed: 02/04/2023] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.
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Affiliation(s)
- Youlin Zhao
- Business School of Hohai University, Nanjing city, Jiangsu Province, PR China
- * E-mail: (YZ); (LG)
| | - Liang Ge
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
- * E-mail: (YZ); (LG)
| | - Yijun Zhou
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Zhongfang Sun
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Erlong Zheng
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Xingmeng Wang
- Tianjin Institute of Surveying and Mapping, Tianjin city, PR China
| | - Yongchun Huang
- Business School of Hohai University, Nanjing city, Jiangsu Province, PR China
| | - Huiping Cheng
- School of Economics and Management, Hubei University of Technology, Wuhan,Hubei Province, PR China
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45
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Sun L, Zou LX. Spatiotemporal analysis and forecasting model of hemorrhagic fever with renal syndrome in mainland China. Epidemiol Infect 2018; 146:1680-1688. [PMID: 30078384 PMCID: PMC9507955 DOI: 10.1017/s0950268818002030] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 05/10/2018] [Accepted: 06/28/2018] [Indexed: 11/07/2022] Open
Abstract
Hemorrhagic fever with renal syndrome (HFRS) caused by hantaviruses is a serious public health problem in China, accounting for 90% of HFRS cases reported globally. In this study, we applied geographical information system (GIS), spatial autocorrelation analyses and a seasonal autoregressive-integrated moving average (SARIMA) model to describe and predict HFRS epidemic with the objective of monitoring and forecasting HFRS in mainland China. Chinese HFRS data from 2004 to 2016 were obtained from National Infectious Diseases Reporting System (NIDRS) database and Chinese Centre for Disease Control and Prevention (CDC). GIS maps were produced to detect the spatial distribution of HFRS cases. The Moran's I was adopted in spatial global autocorrelation analysis to identify the integral spatiotemporal pattern of HFRS outbreaks, while the local Moran's Ii was performed to identify 'hotspot' regions of HFRS at province level. A fittest SARIMA model was developed to forecast HFRS incidence in the year 2016, which was selected by Akaike information criterion and Ljung-Box test. During 2004-2015, a total of 165 710 HFRS cases were reported with the average annual incidence at province level ranged from 0 to 13.05 per 100 000 persons. Global Moran's I analysis showed that the HFRS outbreaks presented spatially clustered distribution, with the degree of cluster gradually decreasing from 2004 to 2009, then turned out to be randomly distributed and reached lowest point in 2012. Local Moran's Ii identified that four provinces in northeast China contributed to a 'high-high' cluster as a traditional epidemic centre, and Shaanxi became another HFRS 'hotspot' region since 2011. The monthly incidence of HFRS decreased sharply from 2004 to 2009 in mainland China, then increased markedly from 2010 to 2012, and decreased again since 2013, with obvious seasonal fluctuations. The SARIMA ((0,1,3) × (1,0,1)12) model was the most fittest forecasting model for the dataset of HFRS in mainland China. The spatiotemporal distribution of HFRS in mainland China varied in recent years; together with the SARIMA forecasting model, this study provided several potential decision supportive tools for the control and risk-management plan of HFRS in China.
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Affiliation(s)
- Ling Sun
- Department of Nephrology, Xuzhou Central Hospital, Medical College of Southeast University, Xuzhou, Jiangsu, China
| | - Lu-Xi Zou
- School of Management, Zhejiang University, Hangzhou, Zhejiang, China
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46
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Zhao Y, Xu Q, Chen Y, Tsui KL. Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach. BMC Infect Dis 2018; 18:398. [PMID: 30103690 PMCID: PMC6090735 DOI: 10.1186/s12879-018-3285-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 07/31/2018] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Hand, foot, and mouth disease (HFMD) has been recognized as one of the leading infectious diseases among children in China, which causes hundreds of annual deaths since 2008. In China, the reports of monthly HFMD cases usually have a delay of 1-2 months due to the time needed for collecting and processing clinical information. This time lag is far from optimal for policymakers making decisions. To alleviate this information gap, this study uses a meta learning framework and combines publicly Internet-based information (Baidu search queries) for real-time estimation of HFMD cases. METHODS We incorporate Baidu index into modeling to nowcast the monthly HFMD incidences in Guangxi, Zhejiang, Henan provinces and the whole China. We develop a meta learning framework to select appropriate predictive model based on the statistical and time series meta features. Our proposed approach is assessed for the HFMD cases within the time period from July 2015 to June 2016 using multiple evaluation metrics including root mean squared error (RMSE) and correlation coefficient (Corr). RESULTS For the four areas: whole China, Guangxi, Zhejiang, and Henan, our approach is superior to the best competing models, reducing the RMSE by 37, 20, 20, and 30% respectively. Compared with all the alternative predictive methods, our estimates show the strongest correlation with the observations. CONCLUSIONS In this study, the proposed meta learning method significantly improves the HFMD prediction accuracy, demonstrating that: (1) the Internet-based information offers the possibility for effective HFMD nowcasts; (2) the meta learning approach is capable of adapting to a wide variety of data, and enables selecting appropriate method for improving the nowcasting accuracy.
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Affiliation(s)
- Yang Zhao
- Centre for System Informatics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, People's Republic of China.
| | - Qinneng Xu
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, People's Republic of China
| | - Yupeng Chen
- Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, People's Republic of China
| | - Kwok Leung Tsui
- Centre for System Informatics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, People's Republic of China.,Department of Systems Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region, People's Republic of China
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Wang H, Tian CW, Wang WM, Luo XM. Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect 2018; 146:935-939. [PMID: 29708082 PMCID: PMC9184947 DOI: 10.1017/s0950268818001115] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Seasonal autoregressive integrated moving average (SARIMA) has been used to model nationwide tuberculosis (TB) incidence in other countries. This study aimed to characterise monthly TB notification rate in China. Monthly TB notification rate from 2005 to 2017 was used. Time-series analysis was based on a SARIMA model and a hybrid model of SARIMA-generalised regression neural network (GRNN) model. A decreasing trend (3.17% per years, P < 0.01) and seasonal variation of TB notification rate were found from 2005 to 2016 in China, with a predominant peak in spring. A SARIMA model of ARIMA (0,1,1) (0,1,1)12 was identified. The mean error rate of the single SARIMA model and the SARIMA-GRNN combination model was 6.07% and 2.56%, and the determination coefficient was 0.73 and 0.94, respectively. The better performance of the SARIMA-GRNN combination model was further confirmed with the forecasting dataset (2017). TB is a seasonal disease in China, with a predominant peak in spring, and the trend of TB decreased by 3.17% per year. The SARIMA-GRNN model was more effective than the widely used SARIMA model at predicting TB incidence.
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Affiliation(s)
- H. Wang
- Kunshan Centers for Disease Control and Prevention, Kunshan, China
| | - C. W. Tian
- Kunshan Centers for Disease Control and Prevention, Kunshan, China
- Author for correspondence: C. W. Tian, E-mail:
| | - W. M. Wang
- Kunshan Centers for Disease Control and Prevention, Kunshan, China
| | - X. M. Luo
- Kunshan Centers for Disease Control and Prevention, Kunshan, China
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Liu S, Chen J, Wang J, Wu Z, Wu W, Xu Z, Hu W, Xu F, Tong S, Shen H. Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2018; 62:565-574. [PMID: 29086082 DOI: 10.1007/s00484-017-1465-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 10/17/2017] [Accepted: 10/20/2017] [Indexed: 06/07/2023]
Abstract
Hand, foot, and mouth disease (HFMD) is a significant public health issue in China and an accurate prediction of epidemic can improve the effectiveness of HFMD control. This study aims to develop a weather-based forecasting model for HFMD using the information on climatic variables and HFMD surveillance in Nanjing, China. Daily data on HFMD cases and meteorological variables between 2010 and 2015 were acquired from the Nanjing Center for Disease Control and Prevention, and China Meteorological Data Sharing Service System, respectively. A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed and validated by dividing HFMD infection data into two datasets: the data from 2010 to 2013 were used to construct a model and those from 2014 to 2015 were used to validate it. Moreover, we used weekly prediction for the data between 1 January 2014 and 31 December 2015 and leave-1-week-out prediction was used to validate the performance of model prediction. SARIMA (2,0,0)52 associated with the average temperature at lag of 1 week appeared to be the best model (R 2 = 0.936, BIC = 8.465), which also showed non-significant autocorrelations in the residuals of the model. In the validation of the constructed model, the predicted values matched the observed values reasonably well between 2014 and 2015. There was a high agreement rate between the predicted values and the observed values (sensitivity 80%, specificity 96.63%). This study suggests that the SARIMA model with average temperature could be used as an important tool for early detection and prediction of HFMD outbreaks in Nanjing, China.
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Affiliation(s)
- Sijun Liu
- Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, 4059, Australia
| | - Jiaping Chen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jianming Wang
- Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhuchao Wu
- Department of Social Medicine and Health Education, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Weihua Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing Medical University, Nanjing, 210008, China
| | - Zhiwei Xu
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, 4059, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, 4059, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, 4059, Australia.
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, 4059, Australia.
| | - Fei Xu
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
- Department of Non-communicable Disease Prevention, Nanjing Municipal Center for Disease Control and Prevention, Nanjing, 210003, China.
| | - Shilu Tong
- School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland, 4059, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, 4059, Australia
| | - Hongbing Shen
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
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Gou F, Liu X, He J, Liu D, Cheng Y, Liu H, Yang X, Wei K, Zheng Y, Jiang X, Meng L, Hu W. Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China. BMC Infect Dis 2018; 18:15. [PMID: 29310596 PMCID: PMC5759838 DOI: 10.1186/s12879-017-2860-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 11/26/2017] [Indexed: 01/25/2023] Open
Abstract
Background To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. Method Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. Results GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. Conclusion Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity’s effect on HFMD showed a non-linear interacting relationship with average temperature.
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Affiliation(s)
- Faxiang Gou
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Xinfeng Liu
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Jian He
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Dongpeng Liu
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Yao Cheng
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Haixia Liu
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Xiaoting Yang
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Kongfu Wei
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Yunhe Zheng
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Xiaojuan Jiang
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China
| | - Lei Meng
- Institute for Communicable Disease Control and Prevention, Gansu Center for Diseases Prevention and Control, Lanzhou, Gansu, China.
| | - Wenbiao Hu
- School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
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50
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Tracking and predicting hand, foot, and mouth disease (HFMD) epidemics in China by Baidu queries. Epidemiol Infect 2017; 145:1699-1707. [PMID: 28222831 DOI: 10.1017/s0950268817000231] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Hand, foot, and mouth disease (HFMD) is highly prevalent in China, and more efficient methods of epidemic detection and early warning need to be developed to augment traditional surveillance systems. In this paper, a method that uses Baidu search queries to track and predict HFMD epidemics is presented, and the outbreaks of HFMD in China during the 60-month period from January 2011 to December 2015 are predicted. The Pearson correlation coefficient (R) of the predictive model and the mean absolute percentage errors between observed HFMD case counts and the predicted number show that our predictive model gives excellent fit to the data. This implies that Baidu search queries can be used in China to track and reliably predict HFMD epidemics, and can serve as a supplement to official systems for HFMD epidemic surveillance.
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