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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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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Playing with and without Biofeedback 5

Playing with and without Biofeedback

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5th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2017

作者： Zafar, M. Abdullah Ahmed, Beena Gutierrez-Osuna, Ricardo Electrical and Computer Engineering Program Texas AandM University at Qatar Doha Qatar Department of Computer Science and Engineering Texas AandM University College StationTX United States

ISBN: (纸本)9781509054824

Casual mobile games enhanced with biofeedback provide a more engaging alternative to traditional stress self-regulation therapies. Previous studies have shown to help players acquire breathing control skills that carry-over into a subsequent cognitively stressful task. We replicated these results with higher power (42 users), and with a more challenging game. We used a Pac-Man style game enhanced with Respiratory Biofeedback, Pac-Man Zen, to compare 1) breathing control during play, 2) the carry-over of said control into a subsequent stressful task and 3) cognitive improvement in a subsequent stressful task against a non-biofeedback version of the game. Results showed that one-time training with the biofeedback version of the game was not only more effective, but also led to improved performance in a later stressful task, both in terms of lowered breathing and improved test scores, compared to the non-biofeedback version. The result is important because it suggests that stimulating, casual biofeedback games can be effective at teaching breathing control, and thus arousal regulation, in a stressful setting. © 2017 IEEE.

关键词： Biofeedback

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Neurocognitive assessment under various human-robot teaming environments 46

Neurocognitive assessment under various human-robot teaming ...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Packy, Anna L. Jayahankar, Jayesh Teymourlouei, Arya Stone, Joshua Oh, Hyuk Katz, Garrett E. Reggia, James A. Gentili, Rodolphe J. University of Maryland Neuroscience and Cognitive Science Program College Park United States University of Maryland Department of Computer Science College Park United States University of Maryland Department of Electrical & Computer Engineering College Park United States University of Maryland Department of Kinesiology College Park United States Syracuse University Department of Electrical Engineering & Computer Science Syracuse United States

ISBN: (纸本)9798350371499

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot's behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams. © 2024 IEEE.

关键词： Man machine systems

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Revamping robotics education to meet 21st century workforce needs- years 1-2 progress reports

Revamping robotics education to meet 21st century workforce ...

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2018 ASEE Conference for Industry and Education Collaboration, CIEC 2018

作者： Sergeyev, Aleksandr Alaraje, Nasser Kuhl, Scott Hooker, Joshua Druschke, Vincent Kinney, Mark Highum, Mark Electrical and Computer Engineering Program School of Technology Computer Science Department Michigan Technological University HoughtonMI49931 United States Bay de Noc Community College

The interdisciplinary nature of robots makes robotics a valuable STEM pedagogical tool. Robots are increasingly used across industry sectors to improve production throughputs while maintaining product quality. The benefits of robotics, however, depend on workers with up-to-date knowledge and skills to maintain and use existing robots, enhance future technologies, and educate users. It is critical that education institutions respond to the demand for robotics specialists by offering courses and professional certification in robotics and automation. This NSF sponsored project introduces a new approach for Industrial Robotics in electrical engineering technology (EET) programs at Michigan Tech and bay de Noc Community college. The curriculum and software developed by this collaboration of two- and four-year institutions match industry needs and provide a replicable model for programs around the US. The project also addresses the need for certified robotic training centers (CRTCs) and provides curriculum and training opportunities for students from other institutions, industry representatives, and displaced workers. In this article, authors report on Year 2 project progress, including the achievements in the developing "RobotRun" robotic simulation software, professional development opportunities for the faculty members from the other institutions, training workshops for K-12 teachers, and robotic one-day camps for high school students. Copyright © 2018, American Society for engineering Education.

关键词： Curricula

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A Graph Neural Network Multi-Task Learning-Based Approach for Detection and Localization of Cyberattacks in Smart Grids 48

A Graph Neural Network Multi-Task Learning-Based Approach fo...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Davis, Katherine Serpedin, Erchin Texas A and M University Department of Electrical and Computer Engineering College StationTX United States Texas A and M University at Qatar Electrical and Computer Engineering Program Doha Qatar Tennessee Technological University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9781728163277

False data injection attacks (FDIAs) on smart power grids' measurement data present a threat to system stability. When malicious entities launch cyberattacks to manipulate the measurement data, different grid components will be affected, which leads to failures. For effective attack mitigation, two tasks are required: determining the status of the system (normal operation/under attack) and localizing the attacked bus/power substation. Existing mitigation techniques carry out these tasks separately and offer limited detection performance. In this paper, we propose a multi-Task learning-based approach that performs both tasks simultaneously using a graph neural network (GNN) with stacked convolutional Chebyshev graph layers. Our results show that the proposed model presents superior system status identification and attack localization abilities with detection rates of 98.5-100% and 99-100%, respectively, presenting improvements of 5-30% compared to benchmarks. © 2023 IEEE.

关键词： Smart power grids

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A multidisciplinary course sequence for first-year engineering students

A multidisciplinary course sequence for first-year engineeri...

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2002 ASEE Annual Conference and Exposition: Vive L'ingenieur

作者： Brockman, Jay B. Fuja, Thomas E. Batill, Stephen M. University of Notre Dame Department of Computer Science University of Notre Dame Department of Electrical Engineering University of Notre Dame Educational Program College of Engineering University of Notre Dame

The University of Notre Dame has developed a new first year engineering program, and central to that program is a two-course sequence entitled "Introduction to engineering Systems." These courses use a sequence of team-based, multidisciplinary projects to introduce students to the engineering profession and to assist them in developing fundamental problem-solving skills common to all engineering disciplines. This paper describes how these new courses were developed and outlines the learning objectives for the courses. It includes details on the implementation of the courses and the four projects that are central to these two courses. The paper details the ongoing assessment activities and the progress toward achieving the various desired outcomes set-forth for the courses.

关键词： Students

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An innovative interface design with smart phone for interactive computer game applications

An innovative interface design with smart phone for interact...

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International IEEE Consumer Electronics Society's Games Innovations Conference, ICE-GIC

作者： Wei-Chin Huang Tennyson Lu Wai-Chi Fang Electrical Engineering and Computer Science Undergraduate Honors Program National Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781457702587

In the latest electrical consuming generation, computers are still mainly tools for paperwork and game platforms, and tablet PC field and smart phone field are two new fields with powerfully market potential. With a high degree of integration and user friendly interfaces, smart phones have become very popular among the general public. Using various sensors, touch pad, and screen of a smart phone, and designing a friendly control interface with software on it, we can achieve the goal that the computer users can interact with a computer via a smart phone. Therefore, this paper discusses the experiment of using a smart phone as a game controller. Through this attempt, we can get some idea and experience from the experiment to simplify the design of smart phones and improve it to develop a controller mainly used with computers or a new game controller.

关键词： Games Smart phones computers Keyboards Mice IEEE 802.11 Standards Microphones

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Lab-in-a-Box: Development of materials to support independent experimentation on concepts from circuits

Lab-in-a-Box: Development of materials to support independen...

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2009 ASEE Annual Conference and Exposition

作者： Meehan, Kathleen Hendricks, Robert Doolittle, Peter Clark, Richard Shek, Carl Virginia Tech United States Departments of Materials Science and Engineering and Electrical Computer Engineering Virginia Tech United States Department of Materials Science Department of Learning Science and Technology School of Education Virginia Tech United States Center for Excellence Undergraduate Teaching Virginia Western Community College United States

A project known as Lab-in-a-Box (LiaB) was developed in 2004 as one of the outcomes of a department-level reform within the Bradley Department of electrical and computer engineering (ECE) at Virginia Tech, addressing a need that was identified through student and employer surveys for concrete examples of fundamental concepts in electrical engineering. LiaB is a set of 'hands-on' exercises in which students design, build, and test at home various d.c. and a.c. circuits using an inexpensive electronics kit, digital multimeter, and a software oscilloscope and, thus, has not require significant resources to implement. The inclusion of LiaB in our ECE curriculum has received overwhelmingly positive comments from the students as well as from faculty members who have used the kits for projects in upper division courses that have been traditionally lecture-based with no lab component and has been adopted by three community colleges. The aim of the first set of experiments that are under development, funded by a grant from the National science Foundation, is to reinforce abstract concepts on first-order and second-order RLC circuits introduced in the companion circuits lecture course. The students construct circuits with physical components rather than symbolic parts in PSpice and determine the time-varying voltage drops and currents in the circuit by direct measurement rather than by plugging values into their calculators. Experiments enable students to explore how the component tolerances, the initial state of the capacitor and/or inductor, and the frequency response of the circuit affects the output signal. An approach to integrate evaluation and assessment is being undertaken, where methods to measure the educational outcomes are considered concurrently with the development of the learning materials. A description of our pedagogical approach to the development of these learning materials and the integration of evaluation and assessment metrics will be described. © Americ

关键词： Students

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