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Wang P, Zhang W, Wang H, Shi C, Li Z, Wang D, Luo L, Du Z, Hao Y. Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model. BMC Infect Dis 2024; 24:265. [PMID: 38408967 PMCID: PMC10898154 DOI: 10.1186/s12879-024-09138-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: 05/31/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance. METHODS Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). RESULTS Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively. CONCLUSIONS The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.
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Affiliation(s)
- Pengyu Wang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Hui Wang
- Department of Infectious Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Congxing Shi
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Zhiqiang Li
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China
| | - Dahu Wang
- Department of Infectious Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Lei Luo
- Department of Infectious Disease Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, China.
| | - Zhicheng Du
- Department of Medical Statistics, School of Public Health & Center for Health Information Research & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
- Guangzhou Joint Research Center for Disease Surveillance and Risk Assessment, Sun Yat-sen University & Guangzhou Center for Disease Control and Prevention, Guangzhou, China.
| | - Yuantao Hao
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China.
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China.
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
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Rajendran EG, Mohd Hairi F, Krishna Supramaniam R, T Mohd TAM. Precision public health, the key for future outbreak management: A scoping review. Digit Health 2024; 10:20552076241256877. [PMID: 39139190 PMCID: PMC11320687 DOI: 10.1177/20552076241256877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 05/07/2024] [Indexed: 08/15/2024] Open
Abstract
Background Precision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed. Objective We aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach. Methods and analysis Articles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative. Results A total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%). Conclusions PPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.
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Affiliation(s)
- Ellappa Ghanthan Rajendran
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Farizah Mohd Hairi
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Rama Krishna Supramaniam
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Chen S, Jie Z, Wang G, Li KC, Yang J, Liu X. A new federated learning-based wireless communication and client scheduling solution for combating COVID-19. COMPUTER COMMUNICATIONS 2023; 206:101-109. [PMID: 37197298 PMCID: PMC10162846 DOI: 10.1016/j.comcom.2023.04.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/21/2023] [Accepted: 04/22/2023] [Indexed: 05/19/2023]
Abstract
Federated learning is a machine learning method that can break the data island. Its inherent privacy-preserving property has an important role in training medical image models. However, federated learning requires frequent communication, which incur high communication costs. Moreover, the data is heterogeneous due to different users' preferences, which may degrade the performance of models. To address the problem of statistical heterogeneity, we propose FedUC, an algorithm to control the uploaded updates for federated learning, where a client scheduling method is made on the basis of weight divergence, update increment, and loss. We also balance the local data of the clients by image augmentation to mitigate the impact of the non-independently identically distribution. The server assigns compression thresholds to the clients based on the weight divergence and update increment of the models for gradient compression to reduce the wireless communication costs. Finally, based on the weight divergence, update increment and accuracy, the server dynamically assigns weights to the model parameters for the aggregation. Simulation and analysis utilizing a publicly available chest disease dataset containing COVID-19 are compared with existing federated learning methods. Experimental results show that our proposed strategy has better training performance in improving model accuracy and reducing wireless communication costs.
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Affiliation(s)
- Shuhong Chen
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong Province, China
| | - Zhiyong Jie
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong Province, China
| | - Guojun Wang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong Province, China
| | - Kuan-Ching Li
- Department of Computer Science and Information Engineering, Providence University, Taizhong, Taiwan Province, China
| | - Jiawei Yang
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong Province, China
| | - Xulang Liu
- School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, Guangdong Province, China
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Sharma S, Gupta YK, Mishra AK. Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5943. [PMID: 37297547 PMCID: PMC10252939 DOI: 10.3390/ijerph20115943] [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: 04/03/2023] [Revised: 05/02/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
The global economy has suffered losses as a result of the COVID-19 epidemic. Accurate and effective predictive models are necessary for the governance and readiness of the healthcare system and its resources and, ultimately, for the prevention of the spread of illness. The primary objective of the project is to build a robust, universal method for predicting COVID-19-positive cases. Collaborators will benefit from this while developing and revising their pandemic response plans. For accurate prediction of the spread of COVID-19, the research recommends an adaptive gradient LSTM model (AGLSTM) using multivariate time series data. RNN, LSTM, LASSO regression, Ada-Boost, Light Gradient Boosting and KNN models are also used in the research, which accurately and reliably predict the course of this unpleasant disease. The proposed technique is evaluated under two different experimental conditions. The former uses case studies from India to validate the methodology, while the latter uses data fusion and transfer-learning techniques to reuse data and models to predict the onset of COVID-19. The model extracts important advanced features that influence the COVID-19 cases using a convolutional neural network and predicts the cases using adaptive LSTM after CNN processes the data. The experiment results show that the output of AGLSTM outperforms with an accuracy of 99.81% and requires only a short time for training and prediction.
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Affiliation(s)
- Shruti Sharma
- Department of Computer Science, Banasthali Vidyapith, Tonk 304022, India;
- School of Technology & Management, SVKM’s Narsee Monji Institute of Management Studies (NMIMS), Indore 452005, India
| | - Yogesh Kumar Gupta
- Department of Computer Science, Banasthali Vidyapith, Tonk 304022, India;
| | - Abhinava K. Mishra
- Molecular, Cellular and Developmental Biology Department, University of California Santa Barbara, Santa Barbara, CA 93106, USA
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Li X, Yu Q, Yang Y, Tang C, Wang J. An evolutionary ensemble model based on GA for epidemic transmission prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-222683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
This paper proposes an evolutionary ensemble model based on a Genetic Algorithm (GAEEM) to predict the transmission trend of infectious diseases based on ensemble again and prediction again. The model utilizes the strong global optimization capability of GA for tuning the ensemble structure. Compared with the traditional ensemble learning model, GAEEM has three main advantages: 1) It is set to address the problems of information leakage in the traditional Stacking strategy and overfitting in the Blending strategy. 2) It uses a GA to optimize the combination of base learners and determine the sub. 3) The feature dimension of the data used in this layer is extended based on the optimal base learner combination prediction information data, which can reduce the risk of underfitting and increase prediction accuracy. The experimental results show that the R2 performance of the model in the six cities data set is higher than all the comparison models by 0.18 on average. The MAE and MSE are lower than 42.98 and 42,689.72 on average. The fitting performance is more stable in each data set and shows good generalization, which can predict the epidemic spread trend of each city more accurately.
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Affiliation(s)
- Xiaoning Li
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Qiancheng Yu
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
- The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, China
| | - Yufan Yang
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Chen Tang
- School of Computer Science and Engineering, North Minzu University, Yinchuan, China
| | - Jinyun Wang
- School of Business, North Minzu University, Yinchuan, China
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Zhang P, Wang Z, Huang Y, Wang M. Dual-grained directional representation for infectious disease case prediction. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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Yildirim E, Cicioğlu M, Çalhan A. Real-time internet of medical things framework for early detection of Covid-19. Neural Comput Appl 2022; 34:20365-20378. [PMID: 35912366 PMCID: PMC9308898 DOI: 10.1007/s00521-022-07582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/28/2022] [Indexed: 01/19/2023]
Abstract
The Covid-19 pandemic is a deadly epidemic and continues to affect all world. This situation dragged the countries into a global crisis and caused the collapse of some health systems. Therefore, many technologies are needed to slow down the spread of the Covid-19 epidemic and produce solutions. In this context, some developments have been made with artificial intelligence, machine learning and deep learning support systems in order to alleviate the burden on the health system. In this study, a new Internet of Medical Things (IoMT) framework is proposed for the detection and early prevention of Covid-19 infection. In the proposed IoMT framework, a Covid-19 scenario consisting of various numbers of sensors is created in the Riverbed Modeler simulation software. The health data produced in this scenario are analyzed in real time with Apache Spark technology, and disease prediction is made. In order to provide more accurate results for Covid-19 disease prediction, Random Forest and Gradient Boosted Tree (GBT) Ensemble Learning classifiers, which are formed by Decision Tree classifiers, are compared for the performance evaluation. In addition, throughput, end-to-end delay results and Apache Spark data processing performance of heterogeneous nodes with different priorities are analyzed in the Covid-19 scenario. The MongoDB NoSQL database is used in the IoMT framework to store big health data produced in real time and use it in subsequent processes. The proposed IoMT framework experimental results show that the GBTs classifier has the best performance with 95.70% training, 95.30% test accuracy and 0.970 area under the curve (AUC) values. Moreover, the promising real-time performances of wireless body area network (WBAN) simulation scenario and Apache Spark show that they can be used for the early detection of Covid-19 disease.
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Affiliation(s)
- Emre Yildirim
- Computer Technology Department, Osmaniye Korkut Ata University, Osmaniye, Turkey
| | - Murtaza Cicioğlu
- Computer Engineering Department, Bursa Uludağ University, Bursa, Turkey
| | - Ali Çalhan
- Computer Engineering Department, Düzce University, Düzce, Turkey
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