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Artificial intelligence for modelling infectious disease epidemics

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

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Orthopaedic Journal of Sports Medicine 2024年第3期12卷 23259671241231609页

作者： Chou, Wen-Yi Cheng, Jai-Hong Lien, Yu-Jui Huang, Tian-Hsiang Ho, Wen-Hsien Chou, Paul Pei-Hsi Doctoral Degree Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Center for Shockwave Medicine and Tissue Engineering Department of Medical Research Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopaedic Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan

Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Study Design: Case-control study. Methods: Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. Results: A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. Conclusion: The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT. © The Author(s) 2024.

关键词： calcific tendinitis data mining extracorporeal shockwave therapy J48 decision tree shoulder

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Influence of environmental variables and anthropogenic activities on soda-saline lakes chemistry in northern Tanzania: A remote sensing and GIS approach

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Heliyon 2025年第2期11卷 e41691页

作者： Lameck, Azaria Stephano Rotich, Brian Ahmed, Abdalrahman Kipkulei, Harison K. Akos, Peto Boros, Emil Doctoral School of Environmental Science The Hungarian University of Agriculture and Life Sciences Páter Károly u. 1 Gödöllő 2100 Hungary Department of Earth Science Mbeya University of Science and Technology PO BOX 131 Mbeya Tanzania Institute of Geomatics and Civil Engineering Faculty of Forestry University of Sopron Bajcsy-Zsilinszky ut. 4 Sopron 9400 Hungary Department of Forest and Environment Faculty of Forest Science and Technology University of Gezira Wad Madani Sudan Institute of Aquatic Ecology Centre for Ecological Research Karolina str. 29. Budapest 1113 Hungary Faculty of Environmental Studies and Resources Development Chuka University P.O. Box 109 Chuka 60400 Kenya Humboldt Universität zu Berlin Faculty of Life Sciences Invalidenstraße 42 Berlin 10115 Germany Department of Geomatic Engineering and Geospatial Information Systems Jomo Kenyatta University of Agriculture and Technology P.O. Box 62000 Nairobi 00200 Kenya University of Augsburg Faculty of Applied Computer Sciences Institute of Geography Alter Postweg 118 Augsburg 86159 Germany

This study employed Remote Sensing and Geographical information Systems to explore the influence of environmental factors and human-induced land use/land cover changes on the chemistry of soda-saline lakes in Northern Tanzania. Satellite-based rainfall data were sourced from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) datasets, and temperature data were obtained from MERRA-2. Monthly precipitation, temperature, and drought conditions in lake watersheds were analyzed from 1981 to 2022, while land use and land cover changes were assessed for 2000, 2014, and 2023. Soil types were acquired from the FAO Digital Soil Map of the World, while geological characteristics were sourced from the US Geological Survey database. The findings revealed that the region's climate is ideal for enhancing evapotranspiration, leading to mineral precipitation, and altering the chemistry of soda-saline lakes. The Standardized Precipitation Evapotranspiration Index revealed increased drought events in the lake basins since 1987, with prolonged drought occurrence between 2000 and 2017. The results also showed that the region is characterized by a variety of soil types, including ferric acrisols, chromic cambisols, calcic cambisols, entisols, inceptisols, eutric fluvisols, distric nitisols, humic nitisols, mollic andosols, ochric andosols, and pellic vertisols. Furthermore, the region is distinguished by diverse geological processes, from Precambrian-Cambrian to tertiary intrusive, triggered by volcanic and tectonic activity. Land use/land cover changes results indicated dynamics in the various classes with an overall decrease in areas under water bodies (−39.80 %), forests (−22.57 %) and bareland (−36.18) while agricultural land (111.01 %) built-up areas (434.72 %), shrubs and grasses (72.77 %) increased in area coverage over the 23 years study period (2000–2023). This study underscores the complex interplay between environmental variables and human activities in sha

关键词： Climate variability Geology Land use /land cover change Soda-saline lakes Soil type

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Bi directional sparse attention recurrent autoencoder based intrusion detection for VANET security with tuna swarm optimization

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Scientific reports 2025年第1期15卷 18406页

作者： R Gopi V Thiruppathy Kesavan Md Jakir Hossen Nor Hidayati Binti Abdul Aziz Faculty of Computer Science & Engineering Dhanalakshmi Srinivasan Engineering College Perambalur Tamil Nadu India. Post Doctoral Fellow Department of Engineering and Technology Multimedia University Malaysia Melaka Malaysia. Faculty of Information Technology Dhanalakshmi Srinivasan Engineering College Perambalur India. Faculty of Engineering and Technology (FET) Center for Advanced Analytics (CAA) COE for Artificial Intelligence Multimedia University Melaka Malaysia. jakir.hossen@mmu.edu.my. Faculty of Engineering and Technology (FET) Center for Advanced Analytics (CAA) COE for Artificial Intelligence Multimedia University Melaka Malaysia.

Vehicular Ad-hoc Networks (VANETs) have garnered a lot of consideration and research in last decades. By enhancing safety and comfort, VANETs are essential to the development of self-driving and semi-self-driving cars. Security risks that are specific to VANET or that are observed in ad hoc networks, however, pose significant difficulties. External communication with the situation through data control and Cooperative Awareness Messages (CAMs) exchanges is crucial for these vehicles. Numerous assaults could possibly target VANETs. VANETs may be vulnerable to several types of assaults. The lack of intrusion detection systems, inadequate encryption methods, insecure communication routes, and inadequate authentication processes are some of the technological flaws in VANETs that make smart vehicles vulnerable to attacks. The security and effectiveness of smart vehicle operations suffer because these flaws allow attackers to intercept, change, or interrupt communication between vehicles. Owing to these security concerns, this study introduces a Bi-directional sparse Attention-recurrent Auto Encoder (BSAR-AE) based Intrusion Detection System (IDS) in VANET. The system uses the Tuna Swarm optimizer (TSO) with the enhancement through the implementation of Deep Neural Network (DNN) based feature selection in VANET. It is a useful technique for resolving issues with deep layer neural networks, like low velocity and overfitting issues in learning. The evolutionary based optimization effectively tuned the hyper parameters of the DL model which will improve the detection accuracy. The system use of the CICIDS2017 dataset, which is associated with actual data and the experimented analysis shows that the proposed model is effectual for detecting the malicious attacks in VANET with improved accuracy of 98.7%.

关键词： Anomaly detection Auto encoder Bidirectional long short term memory networks Intrusion detection system Tuna swarm optimization Vehicular Ad-hoc networks

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Corrigendum to 'Data to evaluate the usability of an interactive system based on a judgment-based model' [Data in Brief 43 (2022) 1-14/ 108418]

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Data in brief 2022年 43卷 108472页

作者： Adeleh Asemi Asefeh Asemi Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya 50603 Kuala Lumpur Malaysia. Doctoral School of Economics Business & Informatics Corvinus University of Budapest 1093 Budapest Fovam ter 8. Hungary.

[This corrects the article DOI: 10.1016/***.2022.108418.].

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Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization

Covid-19 Identification with Chest X-Ray Images merging Hand...

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IEEE International Conference on Computing, Communication and Security (ICCCS)

作者： Rik Das Mohammad Arshad P.K. Manjhi Sudeep D. Thepade Post Graduate Programme in Information Technology Xavier Institute of Social Service Ranchi India Faculty of Science (Computer Applications) Vinoba Bhave University Hazaribag India Vinoba Bhave University Hazaribag India Pimpri Chinchwad College of Engineering Pune Maharashtra

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

Outbreak of COVID-19 pandemic has imposed a major threat to the existence of human lives. High rate of infection due to the lethal virus has caused major sufferings and premature deaths to many budding possibilities. Nevertheless, quick identification of the disease can lead to faster recovery with medical help. Unfortunately, the ratio of COVID infection to methods of early medical identification of disease is quite feeble. This has motivated researchers in designing computer aided diagnosis (CAD) systems which can identify the disease from easily available chest X-Ray images of patients. However, the success rate of identification of COVID cases is not satisfactory since the proposed systems are emphasizing on identifying pneumonia rather than separating the cause of it as COVID or non COVID. In this paper, the authors have investigated the cause of such misclassification and have identified feature generalization as one of the reasons. A feature fusion based approach is proposed by the authors to encourage generalization of input features to CAD for better identification of COVID 19. The reason for proposing the fusion based feature formation is encompassing diverse X-Ray image information by extracting descriptors with assorted techniques and uniting them with fusion. The classification results with fusion based approach have reported substantial improvements compared to individual techniques. This in turn indicates better generalization of input features with feature fusion.

关键词： Feature extraction COVID-19 Lung X-ray imaging Support vector machines Histograms Convolutional neural networks

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GLSD: The Global Large-Scale Ship Database and Baseline Evaluations

arXiv

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arXiv 2021年

作者： Shao, Zhenfeng Wang, Jiaming Deng, Lianbing Huang, Xiao Lu, Tao Luo, Fang Zhang, Ruiqian Lv, Xianwei Dang, Chaoya Ding, Qing Wang, Zhiqiang The State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co. Ltd Guangdong Hengqin New Area 519031 China The Department of Geosciences University of Arkansas FayettevilleAR72701 United States The School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China The School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan430063 China The Chinese Academy of Surveying and Mapping Beijing100036 China

In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks. The designed GLSD database includes a total of 212,357 annotated instances from 152,576 images. Based on the collected images, we propose 13 ship categories that widely exist in international routes. These categories include Sailing boat, Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The motivations of developing GLSD include the following: 1) providing a refine and extensive ship detection database that benefits the object detection community, 2) establishing a database with exhaustive labels (bounding boxes and ship class categories) in a uniform classification scheme, and 3) providing a large-scale ship database with geographic information (covering more than 3000 ports and 33 routes) that benefits multi-modal analysis. In addition, we discuss the evaluation protocols corresponding to image characteristics in GLSD and analyze the performance of selected state-of-the-art object detection algorithms on GSLD, aiming to establish baselines for future studies. More information regarding the designed GLSD can be found at https://***/jiaming-wang/GLSD. © 2021, CC BY.

关键词： Object detection

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The antibacterial properties of paint with the addition of ZnO nanoparticles

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AIP Conference Proceedings 2024年第1期3001卷

作者： Wulandari Agustin Muhammad Thoriq Albari Muhammad Arya Ghifari Muhammad Raffi Ghifari Devi Purnamasari Riso Sari Mandeli Department of Chemistry Faculty Mathematics and Natural Sciences Universitas Negeri Padang Padang Indonesia Department of Information Technology Faculty of Computer Sciences Universitas Brawijaya Malang Indonesia Department of Radiology Engineering Universitas Awal Bros Pekanbaru Indonesia Environmental Science Postgraduate Programme Universitas Negeri Padang Padang Indonesia Center for Advanced Material Processing Artificial Intelligence and Biophysics Informatics (CAMPBIOTICS) Universitas Negeri Padang Padang Indonesia

The purpose of this study was to see how the antibacterial characteristics of ZnO nanoparticles were applied to wall paint to determine the effectiveness of ZnO nanoparticles in killing bacteria. Where ZnO is one of the metal oxides with good anti-bacterial properties through the formation of ROS. By modifying the percentage of ZnO nanoparticles used, namely 3%, 5%, 10%, and 15%, and 0% as a blank or comparison. Antibacterial properties were obtained through evaluation using the TPC method which stands for Total Plate Count. ZnO nanoparticles were obtained by synthesizing using the precipitation method. ZnO nanoparticles were characterized using XRD Instruments to analyze the morphological structure.

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Effect of adding CuO as antibacterial to Portland cement

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AIP Conference Proceedings 2024年第1期3001卷

作者： Rida Oppi Ramadhani Muhammad Thoriq Albari Muhammad Arya Ghifari Muhammad Raffi Ghifari Devi Purnamasari Riso Sari Mandeli Department of Chemistry Faculty Mathematics and Natural Sciences Universitas Negeri Padang Padang Indonesia Department of Information Technology Faculty of Computer Sciences Universitas Brawijaya Malang Indonesia Department of Radiology Engineering Universitas Awal Bros Pekanbaru Indonesia Environmental Science Postgraduate Programme Universitas Negeri Padang Padang Indonesia Center for Advanced Material Processing Artificial Intelligence and Biophysics Informatics (CAMPBIOTICS) Universitas Negeri Padang Padang Indonesia

This research uses Escherichia coli bacteria as the model bacteria to assess the efficacy of adding CuO nanoparticles as an antibacterial agent to Portland cement. The disc diffusion technique is used to assess antibacterial characteristics. Where in humid environmental conditions can trigger health problems in the community in the neighborhood where they live. CuO was added to Portland Cement at different rates of 0%, 1%, 3%, and 5% (%w/w) to conduct the assessment. To achieve a perfect dispersion, the nanoparticles were then combined with water using an ultrasonic mixer before being added to the cement. Additionally, characterisation was performed using XRD in order to establish the size of the CuO nanoparticles and the type of crystal that was generated. SEM was used for morphological investigation and characterisation oof Cement-CuO. The ensuing clear zone, which shows the region where bacterial growth was inhibited, serves as a visual indicator of antibacterial efficacy. The results showed that to produce the best antibacterial effectiveness is with a variation in the addition of CuO as much as 3% which results in a size of 7 mm in the diameter of the inhibitory zone. The presence of resistance power indicates that the resulting antibacterial cement shows an effect on the growth of Escherichia coli bacteria.

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MgO’s effect on wall paint’s antibacterial properties

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AIP Conference Proceedings 2024年第1期3001卷

作者： Sintia Noveliza Muhammad Thoriq Albari Muhammad Arya Ghifari Muhammad Raffi Ghifari Devi Purnamasari Riso Sari Mandeli Department of Chemistry Faculty Mathematics and Natural Sciences Universitas Negeri Padang Padang Indonesia Department of Information Technology Faculty of Computer Sciences Universitas Brawijaya Malang Indonesia Department of Radiology Engineering Universitas Awal Bros Pekanbaru Indonesia Environmental Science Postgraduate Programme Universitas Negeri Padang Padang Indonesia Center for Advanced Material Processing Artificial Intelligence and Biophysics Informatics (CAMPBIOTICS) Universitas Negeri Padang Padang Indonesia

In this study, antibacterial properties were tested with the paint used, namely Wall Paint and Escherichia coli bacteria as test bacteria. Bacterial testing was carried out using the Disc Diffusion method, the test was carried out with variations of MgO on Wall Paint, namely 0%, 1%, 3%, 5%, and 100% for positive control as well as variations in incubation time, namely 1 day, 2 days, and 3 days where the test came from the active ROS (Reactive Oxygen Species) in MgO. Besides that, it is necessary to characterize with XRD which aims to determine the size of the nanoparticles of MgO and the crystal structure formed, also do characterization with XRF to determine the chemical composition and concentration of elements in the sample, in addition to characterization with SEM to determine the surface morphology structure samples at 1,500x, 5,000x, and 10,000x magnifications.

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