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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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Mars helicopter technology demonstrator

Mars helicopter technology demonstrator

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AIAA Atmospheric Flight Mechanics Conference, 2018

作者： Bob Balaram, J. Canham, Timothy Duncan, Courtney Golombek, Matt Grip, Håvard Fjær Johnson, Wayne Maki, Justin Quon, Amelia Stern, Ryan Zhu, David Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States NASA Ames Research Center Moffet Field CA94035 United States Robotics Technologist JPL Mobility and Robotic Systems Section JPL Flight Software and Avionics Systems Section JPL Flight Communications Systems Section JPL Planetary Science Section Robotics Technologist JPL Guidance and Control Section NASA Ames Aeromechanics Office JPL Instrument Software and Science Data Systems Section JPL Spacecraft Mechanical Engineering Section JPL Flight Electronics Section Robotics Electrical Engineer JPL Mobility and Robotic Systems Section

We describe a helicopter that is being developed as a technology demonstrator of Mars aerial mobility. The key design features of the helicopter, associated test infrastructure, and results from a full-scale prototype... 详细信息

ISBN: (数字)9781624105258

ISBN: (纸本)9781624105258

关键词： Antennas

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Mars 2020 surface mission performance analysis: Part 2. surface traversability

Mars 2020 surface mission performance analysis: Part 2. surf...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Ono, Masahiro Heverly, Matthew Rothrock, Brandon Ishimatsu, Takuto Almeida, Eduardo Calef, Fred Soliman, Tariq Williams, Nathan Gengl, Hallie Nicholas, Austin Stilley, Erisa K. Otsu, Kyohei Trautman, Marshall Lange, Robert Milkovich, Sarah Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Research Technologist Mobility and Robotics Systems Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mechanical Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Data Understanding Group Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Geophysics and Planetary Geosciences Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Data Visualization Developer Instrument Software and Science Data Systems PasadenaCA91106 United States Software Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Mission Systems Engineering Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA91106 United States Science Systems Engineer Jet Propulsion Laboratory California Institute of Technology 394B PasadenaCA91106 United States

ISBN: (纸本)9781624105753

– The Mars 2020 Rover Mission (M2020) is characterized by demanding requirement on the distance and time for traveling between scientific Regions Of Interest (ROIs). As a result, surface traversability is one of the major driving factors for the landing site selection of M2020. With the newly developed Mars Terrain Traversability analysis Tools (MTTT), we performed traversability analysis of the eight candidate landing sites with an unprecedented granularity. This paper describes the MTTT analysis capabilityies, as well as how the MTTT capabilities were used to downselect from eight to three candidate landing sites for further evaluation. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Site selection

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Incorporating AEGIS autonomous science into mars science laboratory rover mission operations 15th

Incorporating AEGIS autonomous science into mars science lab...

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15th International Conference on Space Operations, SpaceOps 2018

作者： Francis, Raymond Estlin, Tara Doran, Gary Gaines, Daniel Johnstone, Stephen Montaño, Suzanne Peret, Laurent Mousset, Valérie Gasnault, Olivier Frydenvang, Jens Wiens, Roger C. Schaffer, Steven Pavri, Betina Verma, Vandana Chattopadhyay, Debarati Bornstein, Benjamin Mittal, Nimisha Deflores, Lauren Jet Propulsion Laboratory Machine Learning and Instrument Autonomy Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Intelligence and Space Research Division Los Alamos National Laboratory Los AlamosNM87545 United States Telespazio France FIMOC Toulouse France FIMOC Centre National d’Études Spatiales Toulouse France FIMOC Institut de Recherche en Astrophysique et Planétologie Université de Toulouse CNRS CNES UPS France Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Los Alamos National Laboratory Intelligence and Space Research Division Los AlamosNM87545 United States Jet Propulsion Laboratory Artificial Intelligence Research Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument Operations Engineering Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Robot Operations Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Planning and Sequencing Systems Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Electronics and Software Section California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Science Planning Group California Institute of Technology PasadenaCA91109 United States Jet Propulsion Laboratory Instrument System Engineering Group California Institute of Technology PasadenaCA91109 United States

ISBN: (数字)9781624105623

ISBN: (纸本)9781624105623

The AEGIS (Autonomous Exploration for Gathering Increased science) intelligent targeting software system has been in use on the Mars science Laboratory (MSL) mission since 2016. The system allows on-board autonomous selection of targets for the ChemCam remote geochemistry instrument based on analysis of images taken by the rover. This paper describes the deployment of AEGIS to MSL and the operational use of the system since rollout to science operations in May of 2016. We describe how MSL science Operations have adapted to include autonomous target selection, both in procedures and in exploration strategies, and how mission planners and scientists have used AEGIS autonomy to enhance their work on the MSL mission. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rovers

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Achieving fast operational intelligence in NASA's deep space network through complex event processing 14th

Achieving fast operational intelligence in NASA's deep space...

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14th International Conference on Space Operations, SpaceOps 2016

作者： Choi, Joshua S. Verma, Rishi Malhotra, Shan Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Mission Control Systems Section 4800 Oak Grove Drive M/S: 301-480 PasadenaCA91109-8099 United States Instrument Software and Science Data Systems Section 4800 Oak Grove Drive M/S: 158-242 PasadenaCA91109-8099 United States Planning & Execution Systems Section 4800 Oak Grove Drive M/S: 301-250D PasadenaCA91109-8099 United States

ISBN: (数字)9781624104268

ISBN: (纸本)9781624104268

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the-Sun Operations (FtSO). © 2016 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： NASA

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A revisiting of the anatomy and physiology of the grid 1

A revisiting of the anatomy and physiology of the grid

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1st Workshop on the science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM 2015

作者： Mattmann, Chris A. Computer Science Department University of Southern California Henry Salvatori Computer Science Center 338 Los AngelesCA United States Instrument and Science Data Systems Section Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States

ISBN: (纸本)9781450335669

The "Grid" as defined by Foster and Kesselman was a unifying architecture that engendered a new generation of distribution computation, data sharing, and science. Along the way many "grid" technologies were developed, but their mapping to the principal architecture envisaged by the Grid's authors was never concretely specified. We present a decade's long study of 18+ technologies that claim to be "grid";and ask the question "what makes a grid technology a grid?" This question, along with empirical research using topical software architectural recovery techniques;code and program analysis;clustering and information retrieval, allow the definition of an updated grid architecture that more uniformly captures what was built over this timeframe.

关键词： Anatomy Apache Grid Physiology Reverse engineering

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Erratum to: Making sense of big data in health research: towards an EU action plan

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Genome medicine 2016年第1期8卷 118页

作者： Charles Auffray Rudi Balling Inês Barroso László Bencze Mikael Benson Jay Bergeron Enrique Bernal-Delgado Niklas Blomberg Christoph Bock Ana Conesa Susanna Del Signore Christophe Delogne Peter Devilee Alberto Di Meglio Marinus Eijkemans Paul Flicek Norbert Graf Vera Grimm Henk-Jan Guchelaar Yi-Ke Guo Ivo Glynne Gut Allan Hanbury Shahid Hanif Ralf-Dieter Hilgers Ángel Honrado D Rod Hose Jeanine Houwing-Duistermaat Tim Hubbard Sophie Helen Janacek Haralampos Karanikas Tim Kievits Manfred Kohler Andreas Kremer Jerry Lanfear Thomas Lengauer Edith Maes Theo Meert Werner Müller Dörthe Nickel Peter Oledzki Bertrand Pedersen Milan Petkovic Konstantinos Pliakos Magnus Rattray Josep Redón I Màs Reinhard Schneider Thierry Sengstag Xavier Serra-Picamal Wouter Spek Lea A I Vaas Okker van Batenburg Marc Vandelaer Peter Varnai Pablo Villoslada Juan Antonio Vizcaíno John Peter Mary Wubbe Gianluigi Zanetti European Institute for Systems Biology and Medicine 1 avenue Claude Vellefaux 75010 Paris France. cauffray@***. CIRI-UMR5308 CNRS-ENS-INSERM-UCBL Université de Lyon 50 avenue Tony Garnier 69007 Lyon France. cauffray@***. Luxembourg Centre for Systems Biomedicine University of Luxembourg 7 Avenue des Hauts Fourneaux 4362 Esch-sur-Alzette Luxembourg. rudi.balling@uni.lu. Wellcome Trust Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK. Health Services Management Training Centre Faculty of Health and Public Services Semmelweis University Kútvölgyi út 2 1125 Budapest Hungary. Centre for Personalised Medicine Linköping University 581 85 Linköping Sweden. Translational & Bioinformatics Pfizer Inc. 300 Technology Square Cambridge MA 02139 USA. Institute for Health Sciences IACS - IIS Aragon San Juan Bosco 13 50009 Zaragoza Spain. ELIXIR Wellcome Genome Campus Hinxton Cambridge CB10 1SD UK. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Lazarettgasse 14 AKH BT25.2 1090 Vienna Austria. Department of Laboratory Medicine Medical University of Vienna Lazarettgasse 14 AKH BT25.2 1090 Vienna Austria. Max Planck Institute for Informatics Campus E1 4 66123 Saarbrücken Germany. Príncipe Felipe Research Center C/Eduardo Primo Yúfera 3 46012 Valencia Spain. University of Florida Institute of Food and Agricultural Sciences (IFAS) 2033 Mowry Road Gainesville FL 32610 USA. Bluecompanion Ltd 6 London Street (second floor) London W2 1HR UK. Technology Data & Analytics KPMG Luxembourg Société Coopérative 39 Avenue John F. Kennedy 1855 Luxembourg Luxembourg. Department of Human Genetics Department of Pathology Leiden University Medical Centre Einthovenweg 20 2333 ZC Leiden The Netherlands. Information Technology Department European Organization for Nuclear Research (CERN) 385 Route de Meyrin 1211 Geneva 23 Switzerland. Julius Center for Health Sciences and Primary Care University Medical Center

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Reducing the risk and improving mission success for NASA's human mission to a Near-Earth asteroid: How many robotic surveyors?

Reducing the risk and improving mission success for NASA's h...

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AIAA SPACE Conference and Exposition 2011

作者： Smith, Jeffrey H. Lincoln, William P. Weisbin, Charles R. Jet Propulsion Laboratory California Institute of Technology Mission Systems Concepts Section 4800 Oak Grove Dr. Mail Stop 301-165 Pasadena CA 91109-8099 United States Jet Propulsion Laboratory California Institute of Technology Instrument Software and Science Data Systems Section 4800 Oak Grove Dr. Mail Stop 171-264 Pasadena CA 91109-8099 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Mail Stop 202-204 Pasadena CA 91109-8099 United States

ISBN: (纸本)9781600869532

NASA's recent attention and interest in sending a human mission to land on a Near-Earth asteroid raises the question of need for a robotic surveyor. This paper describes a Bayesian approach for comparing the productivity and cost-risk tradeoffs of sending (versus not sending) one or more robotic surveyor missions prior to a human mission to land on an asteroid. The probability of finding an asteroid suitable for landing was derived from an analysis of more than 1200 asteroids in order to define a quantitative estimate of suitability. The low cost of the surveyors relative to the human mission underlined the multi-surveyor strategy as relatively inexpensive insurance against the risks of encountering an unsuitable asteroid for landing on arrival by a human mission. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Asteroids

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Interplanetary CubeSat architecture and missions

Interplanetary CubeSat architecture and missions

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AIAA SPACE Conference and Exposition 2012

作者： Staehle, Robert L. Blaney, Diana Hemmati, Hamid Jones, Dayton Klesh, Andrew Liewer, Paulett Lazio, Joseph Lo, Martin Wen-Yu Mouroulis, Pantazis Murphy, Neil Pingree, Paula J. Wilson, Thor Puig-Suari, Jordi Williams, Austin Svitek, Tomas Betts, Bruce Friedman, Louis Jet Propulsion Laboratory California Institute of Technology Instruments and Science Data Systems Div MS 306-416 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Mars Exploration Rover MS 264-528 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology MS 161-142 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Deep Space Tracking Systems Group MS 138-308 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Mission Concepts Section MS 301-165S Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Astrophysics and Space Sciences Section MS 169-506 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Tracking Systems and Applications Section MS 138-308 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology High Capability Computing and Modeling Group MS 168-200 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Optical Technology Group Supervisor MS 306-451 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology MS 180-600 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology MS T1723-118 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Flight Instrument Electronics and Smallsat Technology MS T1723-118 Pasadena CA 91109 United States California Polytechnic University San Luis Obispo CA 93407 United States California Polytechnic University Aerospace Engineering San Luis Obispo CA 93407 United States Stellar Exploration Inc 174 Suburban Rd #120 San Luis Obispo CA 93401 United States The Planetary Society 85 South Grand Ave Pasadena CA 91105 United States The Planetary Society 85 South Grand Pasaden

Interplanetary CubeSats could enable small, low-cost missions beyond low Earth orbit. This class is defined by mass

ISBN: (数字)9781600869402

ISBN: (纸本)9781600869402

Interplanetary CubeSats could enable small, low-cost missions beyond low Earth orbit. This class is defined by mass

关键词： NASA

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Supporting operations with distributed data systems: Re-architecture and lessons learned

Supporting operations with distributed data systems: Re-arch...

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SpaceOps 2010 Conference

作者： Henricks, Julia A. Hall, Laverne Fung, Janet G. IOS Instrument Software and Science Data Systems Section Mail Stop 230-260 Pasadena CA 91109 United States Instrument Software and Science Data Systems Section Mail Stop 169-315 Pasadena CA 91109 United States Jet Propulsion Laboratory California Institute of Technology Instrument Software and Science Data Systems Section Mail Stop 168-414 Pasadena CA 91109 United States

ISBN: (纸本)9781624101649

The Multi-mission instrument Processing Laboratory (MIPL) at the Jet Propulsion Laboratory (JPL) supports many deep space planetary (orbiter, fly-by, and in-situ) and observatory missions by processing and distributing instrument data, primarily in the form of science imagery and higher order data products which support operations/observation planning. As the MIPL support has extended to both mission critical and distributed operations, a re-assessment of architectural and hosting options was performed to address risk mitigation, improved reliability, extended availability, and changes in Mission Support Area (MSA) design requirements. In response to this paradigm shift from centralized to distributed operations, this paper will discuss the approach to: 1) re-architect MIPL-hosted data processing/distribution software builds and storage capabilities within an integrated development, test, and operations environment, 2) conversion to a service center to consolidate management of disparate funding sources for infrastructure data systems services, 3) lessons learned from moving MRO science data distribution services from the MIPL to a project managed environment, and 4) the challenges ahead. © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Orbits

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