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McClymont H, Lambert SB, Barr I, Vardoulakis S, Bambrick H, Hu W. Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic. J Epidemiol Glob Health 2024:10.1007/s44197-024-00272-y. [PMID: 39141074 DOI: 10.1007/s44197-024-00272-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/26/2024] [Indexed: 08/15/2024] Open
Abstract
The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.
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Affiliation(s)
- Hannah McClymont
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia
| | - Stephen B Lambert
- Communicable Diseases Branch, Queensland Health, Brisbane, Australia
- National Centre for Immunisation Research and Surveillance, Sydney Children's Hospitals Network, Westmead, Australia
| | - Ian Barr
- WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, Melbourne, Australia
| | - Sotiris Vardoulakis
- Health Research Institute, University of Canberra, Canberra, Australia
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health (ECAPH) Research Group, School of Public Health and Social Work, Queensland University of Technology (QUT), Brisbane, Australia.
- Healthy Environments and Lives (HEAL) National Research Network, Canberra, Australia.
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Lv Y, Niu L, Li Q, Shao W, Yan X, Li Y, Yue Y, Chen H. Identification of an immune-related eRNA prognostic signature for clear cell renal cell carcinoma. Aging (Albany NY) 2024; 16:2232-2248. [PMID: 38289619 PMCID: PMC10911372 DOI: 10.18632/aging.205479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 12/01/2023] [Indexed: 02/22/2024]
Abstract
BACKGROUND Immune-related enhancer RNAs (eRNAs) have garnered significant attention in cancer metabolism research, yet their specific roles in ccRCC have remained elusive. METHODS We retrieved eRNA expression profiles from TCGA database and identified immune-related eRNAs (IREs) by assessing their co-expression with immune genes. Utilizing consensus clustering, we organized these IREs into two distinct clusters. The construction of an IREs signature was accomplished through the LASSO and multivariate Cox analysis. Furthermore, we performed Cell Counting Kit-8 and clonogenic assays to assess changes in the proliferative capacity of Caki-1 and 769-P cells. RESULTS The existence of two clusters of immune-related eRNAs in ccRCC, each with distinctive prognostic and immunological attributes. Cluster B exhibited immunosuppressive properties and displayed a positive correlation with immunosuppressive cells. Functional enrichment analysis unveiled their involvement in several tumor-promoting pathways, metabolic pathways and immune pathways. The IREs signature demonstrated its potential to accurately predict patient immune and prognostic characteristics. AC003092.1, an eRNA strongly associated with patient survival, emerged as a potential oncogene significantly linked to adverse prognosis and the presence of immunosuppressive cells and checkpoints in ccRCC patients. Notably, AC003092.1 displayed marked upregulation in ccRCC tissues and cell lines, and its knockdown substantially inhibited the proliferation of Caki-1 and 769-P cells. CONCLUSION We established a robust predictive model that played a vital role in determining the prognosis, clinicopathological characteristics and immune cell infiltration patterns of ccRCC patients. IRE, particularly AC003092.1, which was strongly associated with survival, hold promise as novel immunotherapeutic targets for ccRCC.
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Affiliation(s)
- Yang Lv
- Department of Urology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou 215228, China
| | - Lili Niu
- Central Laboratory, First Affiliated Hospital, Institute (College) of Integrative Medicine, Dalian Medical University, Dalian 116021, China
- Department of Pharmacy, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China
| | - Qiang Li
- Department of Urology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou 215228, China
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wenchuan Shao
- Department of Urology, The State Key Lab of Reproductive, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xinghan Yan
- Department of Urology, The State Key Lab of Reproductive, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yang Li
- Department of Urology, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China
| | - Yulin Yue
- Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Hongqi Chen
- Department of Urology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou 215228, China
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Yao T, Chen X, Wang H, Gao C, Chen J, Yi D, Wei Z, Yao N, Li Y, Yi D, Wu Y. Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates. BMC Bioinformatics 2024; 25:38. [PMID: 38262917 PMCID: PMC10804580 DOI: 10.1186/s12859-023-05621-5] [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: 05/09/2022] [Accepted: 12/15/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.
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Affiliation(s)
- Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Haojia Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Jia Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ning Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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Zhao Y, Zhu Z, Chen B, Qiu S, Huang J, Lu X, Yang W, Ai C, Huang K, He C, Jin Y, Liu Z, Wang FY. Toward parallel intelligence: An interdisciplinary solution for complex systems. Innovation (N Y) 2023; 4:100521. [PMID: 37915363 PMCID: PMC10616416 DOI: 10.1016/j.xinn.2023.100521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 10/03/2023] [Indexed: 11/03/2023] Open
Abstract
The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in the artificial systems, computational experiments, and parallel execution (ACP) approach has been developed. The method cultivates a cycle termed parallel intelligence, which iteratively creates data, acquires knowledge, and refines the actual system. Over the past two decades, the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines, offering versatile interdisciplinary solutions for complex systems across diverse fields. This review explores the origins and fundamental concepts of the parallel systems method, showcasing its accomplishments as a diverse array of parallel technologies and applications while also prognosticating potential challenges. We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.
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Affiliation(s)
- Yong Zhao
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Zhengqiu Zhu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Bin Chen
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Sihang Qiu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Jincai Huang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Xin Lu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Weiyi Yang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Chuan Ai
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
| | - Kuihua Huang
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Cheng He
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai 200032, China
- Institute of Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Yucheng Jin
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
| | - Zhong Liu
- College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
- Hunan Institute of Advanced Technology, Changsha 410073, China
| | - Fei-Yue Wang
- State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Ye W, Chen X, Li P, Tao Y, Wang Z, Gao C, Cheng J, Li F, Yi D, Wei Z, Yi D, Wu Y. OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features. Front Neurol 2023; 14:1158555. [PMID: 37416306 PMCID: PMC10321134 DOI: 10.3389/fneur.2023.1158555] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 05/22/2023] [Indexed: 07/08/2023] Open
Abstract
Background Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.
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Affiliation(s)
- Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Pengpeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yongjun Tao
- Department of Neurology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jian Cheng
- Department of Radiology, Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dali Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- Department of Health Education, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
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Wang Z, Ye W, Chen X, Li Y, Zhang L, Li F, Yao N, Gao C, Wang P, Yi D, Wu Y. Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China. BMC Public Health 2023; 23:1155. [PMID: 37322467 PMCID: PMC10268402 DOI: 10.1186/s12889-023-15945-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 05/20/2023] [Indexed: 06/17/2023] Open
Abstract
OBJECTIVE Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.
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Affiliation(s)
- Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ling Zhang
- Department of Health Education, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Fang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Ning Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Pengyu Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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