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Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

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Journal of Theoretical Biology 2023年 557卷 111332页

作者： Dangerfield, Ciara E. David Abrahams, I. Budd, Chris Butchers, Matt Cates, Michael E. Champneys, Alan R. Currie, Christine S.M. Enright, Jessica Gog, Julia R. Goriely, Alain Déirdre Hollingsworth, T. Hoyle, Rebecca B. INI Professional Services Isham, Valerie Jordan, Joanna Kaouri, Maha H. Kavoussanakis, Kostas Leeks, Jane Maini, Philip K. Marr, Christie Merritt, Clare Mollison, Denis Ray, Surajit Thompson, Robin N. Wakefield, Alexandra Wasley, Dawn Isaac Newton Institute to Mathematical Sciences University of Cambridge United Kingdom Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium United Kingdom Department for Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics University of Bath United Kingdom Department of Engineering Mathematics University of Bristol United Kingdom School of Mathematical Sciences University of Southampton United Kingdom School of Computing Science University of Glasgow United Kingdom Mathematical Institute University of Oxford United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford United Kingdom Department of Statistical Science University College London United Kingdom Independent United Kingdom EPCC The University of Edinburgh United Kingdom Department of Actuarial Mathematics and Statistics Heriot-Watt University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Mathematics Institute University of Warwick United Kingdom Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research University of Warwick United Kingdom The Royal Society London United Kingdom International Centre for Mathematical Sciences University of Edinburgh & Heriot-Watt University United Kingdom

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”. © 2022 The Author(s)

关键词： COVID-19 knowledge exchange Mathematical modelling Research co-ordination

Collective dynamics of phase-repulsive oscillators solves graph coloring problem

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arXiv 2019年

作者： Crnkić, Aladin Povh, Janez Jaćimović, Vladimir Levnajić, Zoran Faculty of Technical Engineering University of Bihać Faculty of Mechanical Engineering University of Ljubljana Slovenia Faculty of Natural Sciences and Mathematics University of Montenegro Podgorica Montenegro Complex systems and Data science Lab Faculty of information studies in Novo mesto Slovenia Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

Recent years saw a fast development of quantum algorithms that solve combinatorial optimization problems, such as graph coloring, by finding the ground state of a suitable quantum system. Inspired by this, we show how to couple phase-oscillators on a graph so that collective dynamics ‘searches’ for the coloring of that graph as it relaxes towards the dynamical equilibrium. This translates a combinatorial optimization problem (graph coloring) into a continuous optimization problem (finding and evaluating the global minimum of dynamical non-equilibrium potential, done by the natural system’s evolution). Using a sample of graphs we show that our method performs at least as good as traditional combinatorial algorithms and can serve as a viable alternative to them. Moreover, we show that with the same computational cost our method efficiently solves the harder problem of improper coloring of weighed graphs. Copyright © 2019, The Authors. All rights reserved.

关键词： Graph theory

Translating the grid: How a translational approach shaped the development of grid computing

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arXiv 2020年

作者： Foster, Ian Kesselman, Carl Data Science and Learning Division Argonne National Lab LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Information Sciences Institute Marina del ReyCA90292 United States Department of Industrial and Systems Engineering U. Southern California Los AngelesCA90089 United States

A growing gap between progress in biological knowledge and improved health outcomes inspired the new discipline of translational medicine, in which the application of new knowledge is an explicit part of a research plan. Abramson and Parashar argue that a similar gap between complex computational technologies and ever-more-challenging applications demands an analogous discipline of translational computer science, in which the deliberate movement of research results into large-scale practice becomes a central research focus rather than an afterthought. We revisit from this perspective the development and application of grid computing from the mid-1990s onwards, and find that a translational framing is useful for understanding the technology’s development and impact. We discuss how the development of grid computing infrastructure, and the Globus Toolkit, in particular, benefited from a translational approach. We identify lessons learned that can be applied to other translational computer science initiatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Grid computing

Application Aspects of Fuzzy Color Modeling

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Application Aspects of Fuzzy Color Modeling

IEEE International Conference on systems, Man and Cybernetics

作者： Pakizar Shamoi Atsushi Inoue Hiroharu Kawanaka Faculty of Information Technologies Kazakh-British Technical University Almaty Kazakhstan Department of Information Systems and Data Analytics Eastern Washington University Washington USA Graduate School of Engineering Mie University Tsu Japan

Currently, processing data and presenting results in a human consistent manner is of great value. This paper provides some insights on application aspects of fuzzy color modeling in e-commerce apparel coordination and computational aesthetics. For that, we apply previously developed FHSI (Fuzzy HSI) color model. In FHSI, colors are modeled considering the imprecision, subjectivity, context-dependency, and non-uniformity of color distributions. Developed prototype of apparel online shop with underlying fuzzy color processing mechanisms allows overcoming the limitations of the traditional e-commerce search. Our method is effectively used to retrieve shopping items based on fuzzy dominant colors, make compositions based on harmonious color schemes we derived, predict the aesthetic preference for color combinations from an individual color preference and harmony.

关键词： Image color analysis Linguistics Visualization Computational modeling Clothing Libraries Semantics

Using machine learning to predict near-term mortality in cirrhosis patients hospitalized at the University of Virginia health system

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Using machine learning to predict near-term mortality in cir...

2018 systems and information engineering Design Symposium, SIEDS 2018

作者： Harrison, Elizabeth Chang, Myron Hao, Yi Flower, Abigail School of Medicine and Data Science Institute University of Virginia United States Data Science Institute University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

This study aimed to predict near-term mortality in patients hospitalized with cirrhosis at the University of Virginia Health System using two approaches - logistic regression and a long short-term memory neural network, with an emphasis on the latter. Using data from de-identified medical record entries for over 500 patient stays in the intensive care unit, a recurrent long short-term memory neural network was trained to predict whether a mortality would occur between 12 and 24 hours from the time of prediction. This was achieved by feeding the network a sequence of consecutive observations for each patient in the training set, thereby allowing the network to 'look back' and identify increasing mortality risk based on changes in vital signs and laboratory results over time. Performance of the neural network model was compared to that of a logistic regression model and the Chronic Liver Failure Consortium Acute Decompensation Score, a benchmark scoring method for mortality risk in cirrhosis patients. Although the primary model continues to need refinement, results indicate that the neural network and logistic regression models both outperform the Chronic Liver Failure Score. As more data are made available, further improvement in the performance of the neural network is anticipated. © 2018 IEEE.

关键词： Forecasting

Nanotheranostics to target antibiotic-resistant bacteria: Strategies and applications

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OpenNano 2023年 11卷

作者： Bhattacharjee, Rahul Negi, Arvind Bhattacharya, Basudha Dey, Tanima Mitra, Priya Preetam, Subham Kumar, Lamha Kar, Sulagna Das, Sabya Sachi Iqbal, Danish Kamal, Mehnaz Alghofaili, Fayez Malik, Sumira Dey, Abhijit Jha, Saurabh Kumar Ojha, Shreesh Paiva-Santos, Ana Cláudia Kesari, Kavindra Kumar Jha, Niraj Kumar KIIT School of Biotechnology Kalinga Institute of Industrial Technology (KIIT-DU) Odisha Bhubaneswar India Department of Bioproducts and Bio-systems School of Chemical Engineering Aalto University Espoo 00076 Finland Institute of Advanced Materials IAAM Gammalkilsvägen 18 Ulrika 59053 Sweden School of Biology Indian Institute of Science Education and Research Thiruvananthapuram India School of Pharmaceutical and Population Health Informatics DIT University Uttarakhand Dehradun 248009 India Department of Health Information Management College of Applied Medical Sciences Buraydah Private Colleges Buraydah 51418 Saudi Arabia Department of Pharmaceutical Chemistry College of Pharmacy Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Medical Laboratory Sciences College of Applied Medical Sciences Majmaah University Majmaah 11952 Saudi Arabia Amity Institute of Biotechnology Amity University Jharkhand Jharkhand Ranchi 834001 India Department of Life Sciences Presidency University 86/1 College Street West Bengal Kolkata 700073 India Department of Biotechnology School of Engineering and Technology Sharda University Knowledge Park III Uttar Pradesh Greater Noida India Department of Pharmacology and Therapeutics College of Medicine and Health Sciences United Arab Emirates University Al Ain P.O. Box 15551 United Arab Emirates Department of Pharmaceutical Technology Faculty of Pharmacy of the University of Coimbra University of Coimbra Coimbra Portugal REQUIMTE/LAQV Group of Pharmaceutical Technology Faculty of Pharmacy of the University of Coimbra University of Coimbra Coimbra Portugal Department of Applied Physics School of Science Aalto University Espoo 00076 Finland Department of Biotechnology School of Applied & Life Sciences (SALS) Uttaranchal University Dehradun 248007 India School of Bioengineering and Biosciences Lovely Professional University Punjab Phagwara 144411 India

Various health agencies, such as the European Medical Agency (EMA), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO), timely cited the upsurge of antibiotic resistance as a severe threat to the public health and global economy. Importantly, there is a rise in nosocomial infections among covid-19 patients and in-hospitalized patients with the delineating disorder. Most of nosocomial infections are related to the bacteria residing in biofilm, which are commonly formed on material surfaces. In biofilms, microcolonies of various bacteria live in syntropy;therefore, their infections require a higher antibiotic dosage or cocktail of broad-spectrum antibiotics, aggravating the severity of antibiotic resistance. Notably, the lack of intrinsic antibacterial properties in commercial-grade materials desires to develop newer functionalized materials to prevent biofilm formation on their surfaces. To devise newer strategies, materials prepared at the nanoscale demonstrated reasonable antibacterial properties or enhanced the activity of antimicrobial agents (that are encapsulated/chemically functionalized onto the material surface). In this manuscript, we compiled such nanosized materials, specifying their role in targeting specific strains of bacteria. We also enlisted the examples of nanomaterials, nanodevice, nanomachines, nano-camouflaging, and nano-antibiotics for bactericidal activity and their possible clinical implications. © 2023 The Author(s)

关键词： Antibacterial Antibiotic drug resistance Nano-antibiotics Nano-device Nano-materials Nano-robots Nano-sized materials

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

Industrial topics in urban labor system

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arXiv 2020年

作者： Park, Jaehyuk Frank, Morgan R. Sun, Lijun Youn, Hyejin Kellogg School of Management Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems EvanstonIL United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburgePA United States Connection Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Digital Economy Lab Institute for Human-Centered AI Stanford University StanfordCA United States Department of Civil Engineering McGill University MontrealQC Canada Department of Civil and Environmental Engineering Northwestern University EvanstonIL60208 United States

Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification systems is not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics — clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders especially in our fast-changing economy. Copyright © 2020, The Authors. All rights reserved.

关键词： Employment

"Is this an STD? Please help!⇝ online information seeking for sexually transmitted diseases on reddit 12

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"Is this an STD? Please help!⇝ online information seeking f...

12th International AAAI Conference on Web and Social Media, ICWSM 2018

作者： Nobles, Alicia L. Dreisbach, Caitlin N. Keim-Malpass, Jessica Barnes, Laura E. Department of Systems and Information Engineering United States School of Nursing United States Data Science Institute United States University of Virginia United States

ISBN: (纸本)9781577357988

Increasing incidence of sexually transmitted diseases (STDs) has prompted the public health and technology communities to innovate new measures to understand how individuals use Internet resources to attain relevant information, particularly for sensitive or stigmatized conditions. The purpose of this study is to examine recent health information seeking and needs of the r/STD community, a subreddit focused exclusively on STDs. We found that the majority of posts crowdsource information about intermediate, non-reportable STDs such as human papillomavirus (HPV). Crowdsourced information in this community focused on symptoms, treatment, as well as the social and emotional aspects of sexual health such as fear of misdiagnosis. From our analysis, it is clear that online communities focused on discussion of health symptoms have the ripe potential to influence information-seeking behavior and consumer action. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diseases