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Marwaha JS, Downing M, Halamka J, Abernethy A, Franklin JB, Anderson B, Kohane I, Wagholikar K, Brownstein J, Haendel M, Brat GA. Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data. HEALTHCARE (AMSTERDAM, NETHERLANDS) 2024; 12:100738. [PMID: 38531228 DOI: 10.1016/j.hjdsi.2024.100738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/05/2023] [Accepted: 02/22/2024] [Indexed: 03/28/2024]
Abstract
The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic. Insights have been compiled across five dimensions: consortium composition, governance structure and alignment of priorities, data sharing, data analysis, and evidence dissemination. The purpose of this piece is to offer guidance on building large-scale multi-institutional RWD analysis pipelines for future public health issues. The composition of each consortium was largely influenced by existing collaborations. A central set of priorities for evidence generation guided each consortium, however different approaches to governance emerged. Challenges surrounding limited access to clinical data due to various contributors were overcome in unique ways. While all consortia used different methods to construct and analyze patient cohorts ranging from centralized to federated approaches, all proved effective for generating meaningful real-world evidence. Actionable recommendations for clinical practice and public health agencies were made from translating insights from consortium analyses. Each consortium was successful in rapidly answering questions about COVID-19 diagnosis and treatment despite all taking slightly different approaches to data sharing and analysis. Leveraging RWD, leveraged in a manner that applies scientific rigor and transparency, can complement higher-level evidence and serve as an important adjunct to clinical trials to quickly guide policy and critical care, especially for a pandemic response.
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Affiliation(s)
- Jayson S Marwaha
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Maren Downing
- Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA; Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | | | | | | | | | | | | | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus School of Medicine, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, USA.
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2
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Lee JS, Tyler ARB, Veinot TC, Yakel E. Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response. JMIR Public Health Surveill 2024; 10:e51880. [PMID: 38656780 PMCID: PMC11079773 DOI: 10.2196/51880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 04/26/2024] Open
Abstract
During public health crises, the significance of rapid data sharing cannot be overstated. In attempts to accelerate COVID-19 pandemic responses, discussions within society and scholarly research have focused on data sharing among health care providers, across government departments at different levels, and on an international scale. A lesser-addressed yet equally important approach to sharing data during the COVID-19 pandemic and other crises involves cross-sector collaboration between government entities and academic researchers. Specifically, this refers to dedicated projects in which a government entity shares public health data with an academic research team for data analysis to receive data insights to inform policy. In this viewpoint, we identify and outline documented data sharing challenges in the context of COVID-19 and other public health crises, as well as broader crisis scenarios encompassing natural disasters and humanitarian emergencies. We then argue that government-academic data collaborations have the potential to alleviate these challenges, which should place them at the forefront of future research attention. In particular, for researchers, data collaborations with government entities should be considered part of the social infrastructure that bolsters their research efforts toward public health crisis response. Looking ahead, we propose a shift from ad hoc, intermittent collaborations to cultivating robust and enduring partnerships. Thus, we need to move beyond viewing government-academic data interactions as 1-time sharing events. Additionally, given the scarcity of scholarly exploration in this domain, we advocate for further investigation into the real-world practices and experiences related to sharing data from government sources with researchers during public health crises.
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Affiliation(s)
- Jian-Sin Lee
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | | | - Tiffany Christine Veinot
- School of Information, University of Michigan, Ann Arbor, MI, United States
- Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, United States
- Department of Learning Health Sciences, School of Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Elizabeth Yakel
- School of Information, University of Michigan, Ann Arbor, MI, United States
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3
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Rainey JJ, Lin XM, Murphy S, Velazquez-Kronen R, Do T, Hughes C, Harris AM, Maitland A, Gundlapalli AV. Deployment of the National Notifiable Diseases Surveillance System during the 2022-23 mpox outbreak in the United States-Opportunities and challenges with case notifications during public health emergencies. PLoS One 2024; 19:e0300175. [PMID: 38603766 PMCID: PMC11008850 DOI: 10.1371/journal.pone.0300175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 02/23/2024] [Indexed: 04/13/2024] Open
Abstract
Timely case notifications following the introduction of an uncommon pathogen, such as mpox, are critical for understanding disease transmission and for developing and implementing effective mitigation strategies. When Massachusetts public health officials notified the Centers for Disease Control and Prevention (CDC) about a confirmed orthopoxvirus case on May 17, 2023, which was later confirmed as mpox at CDC, mpox was not a nationally notifiable disease. Because existing processes for new data collections through the National Notifiable Disease Surveillance System were not well suited for implementation during emergency responses at the time of the mpox outbreak, several interim notification approaches were established to capture case data. These interim approaches were successful in generating daily case counts, monitoring disease transmission, and identifying high-risk populations. However, the approaches also required several data collection approvals by the federal government and the Council for State and Territorial Epidemiologists, the use of four different case report forms, and the establishment of complex data management and validation processes involving data element mapping and record-level de-duplication steps. We summarize lessons learned from these interim approaches to inform and improve case notifications during future outbreaks. These lessons reinforce CDC's Data Modernization Initiative to work in close collaboration with state, territorial, and local public health departments to strengthen case-based surveillance prior to the next public health emergency.
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Affiliation(s)
- Jeanette J. Rainey
- Division of Global Health Security, Global Health Center, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Xia Michelle Lin
- Detect and Monitor Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Sylvia Murphy
- Detect and Monitor Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Raquel Velazquez-Kronen
- Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, United States of America
| | - Tuyen Do
- Office of the Director, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Christine Hughes
- Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Aaron M. Harris
- Detect and Monitor Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
| | - Aaron Maitland
- Division of Health Interview Statistics, National Center of Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD, United States of America
| | - Adi V. Gundlapalli
- Office of the Director, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, GA, United States of America
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4
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Wiltz JL, Lee B, Kaufmann R, Carney TJ, Davis K, Briss PA. Modernizing CDC's Practices and Culture for Better Data Sharing, Impact, and Transparency. Prev Chronic Dis 2024; 21:E18. [PMID: 38512778 PMCID: PMC10962273 DOI: 10.5888/pcd21.230200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024] Open
Affiliation(s)
- Jennifer L Wiltz
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Hwy, Mail Stop S107-8, Atlanta, GA 30341
- US Public Health Service, Bethesda, Maryland
| | - Brian Lee
- Office of the Chief Information Officer, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Rachel Kaufmann
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Timothy J Carney
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Kailah Davis
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Peter A Briss
- National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia
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5
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Neves A, Cuesta I, Hjerde E, Klemetsen T, Salgado D, van Helden J, Rahman N, Fatima N, Karathanasis N, Zmora P, Åkerström WN, Grellscheid SN, Waheed Z, Blomberg N. FAIR+E pathogen data for surveillance and research: lessons from COVID-19. Front Public Health 2023; 11:1289945. [PMID: 38074768 PMCID: PMC10703184 DOI: 10.3389/fpubh.2023.1289945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 10/30/2023] [Indexed: 12/18/2023] Open
Abstract
The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many hurdles within the organizational, scientific, technical and cultural realms still remain to be tackled to be prepared for future threats. We propose to (i) continue supporting global efforts that have proven to be efficient and trustworthy toward addressing challenges in pathogen molecular data sharing; (ii) establish a distributed network of Pathogen Data Platforms to (a) ensure high quality data, metadata standardization and data analysis, (b) perform data brokering on behalf of data providers both for research and surveillance, (c) foster capacity building and continuous improvements, also for pandemic preparedness; (iii) establish an International One Health Pathogens Portal, connecting pathogen data isolated from various sources (human, animal, food, environment), in a truly One Health approach and following FAIR principles. To address these challenging endeavors, we have started an ELIXIR Focus Group where we invite all interested experts to join in a concerted, expert-driven effort toward sustaining and ensuring high-quality data for global surveillance and research.
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Affiliation(s)
- Aitana Neves
- SIB Swiss Institute of Bioinformatics, Clinical Bioinformatics, Geneva, Switzerland
| | - Isabel Cuesta
- Bioinformatics Unit, Institute of Health Carlos III, Madrid, Spain
| | - Erik Hjerde
- Institute of Chemistry, The Arctic University of Norway, Tromsø, Norway
| | - Terje Klemetsen
- Institute of Chemistry, The Arctic University of Norway, Tromsø, Norway
| | - David Salgado
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
| | - Jacques van Helden
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
- Aix-Marseille Univ, INSERM, Lab. Theory and Approaches of Genome Complexity (TAGC), Marseille, France
| | - Nadim Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Nazeefa Fatima
- ELIXIR Norway, Centre for Bioinformatics, University of Oslo, Oslo, Norway
| | - Nestoras Karathanasis
- Bioinformatics Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Pawel Zmora
- Department of Molecular Virology, Institute of Bioorganic Chemistry Polish Academy of Sciences, Poznan, Poland
| | - Wolmar Nyberg Åkerström
- NBIS National Bioinformatics Infrastructure Sweden, SciLifeLab, Uppsala University, Uppsala, Sweden
| | | | - Zahra Waheed
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Niklas Blomberg
- ELIXIR Hub, Wellcome Genome Campus, Cambridge, United Kingdom
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6
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Bhatia S, Imai N, Watson OJ, Abbood A, Abdelmalik P, Cornelissen T, Ghozzi S, Lassmann B, Nagesh R, Ragonnet-Cronin ML, Schnitzler JC, Kraemer MU, Cauchemez S, Nouvellet P, Cori A. Lessons from COVID-19 for rescalable data collection. THE LANCET. INFECTIOUS DISEASES 2023; 23:e383-e388. [PMID: 37150186 PMCID: PMC10159580 DOI: 10.1016/s1473-3099(23)00121-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 05/09/2023]
Abstract
Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
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Affiliation(s)
- Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK; Modelling and Economics Unit, UK Health Security Agency, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK
| | - Oliver J Watson
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | - Stéphane Ghozzi
- Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Britta Lassmann
- ProMED-mail, International Society for Infectious Diseases, Brookline, MA, USA
| | - Radhika Nagesh
- Blavatnik School of Government, University of Oxford, Oxford, UK
| | - Manon L Ragonnet-Cronin
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA
| | | | - Moritz Ug Kraemer
- Department of Biology, University of Oxford, Oxford, UK; Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR2000, Paris, France
| | - Pierre Nouvellet
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; School of Life Sciences, University of Sussex, Brighton, UK
| | - Anne Cori
- MRC Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, London, UK; NIHR Health Protection Research Unit in Modelling and Health Economics, Imperial College London, UK Health Security Agency, and London School of Hygiene & Tropical Medicine, London, UK.
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7
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Giovanetti M, Branda F, Cella E, Scarpa F, Bazzani L, Ciccozzi A, Slavov SN, Benvenuto D, Sanna D, Casu M, Santos LA, Lai A, Zehender G, Caccuri F, Ianni A, Caruso A, Maroutti A, Pascarella S, Borsetti A, Ciccozzi M. Epidemic history and evolution of an emerging threat of international concern, the severe acute respiratory syndrome coronavirus 2. J Med Virol 2023; 95:e29012. [PMID: 37548148 DOI: 10.1002/jmv.29012] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/12/2023] [Accepted: 07/20/2023] [Indexed: 08/08/2023]
Abstract
This comprehensive review focuses on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its impact as the cause of the COVID-19 pandemic. Its objective is to provide a cohesive overview of the epidemic history and evolutionary aspects of the virus, with a particular emphasis on its emergence, global spread, and implications for public health. The review delves into the timelines and key milestones of SARS-CoV-2's epidemiological progression, shedding light on the challenges encountered during early containment efforts and subsequent waves of transmission. Understanding the evolutionary dynamics of the virus is crucial in monitoring its potential for adaptation and future outbreaks. Genetic characterization of SARS-CoV-2 is discussed, with a focus on the emergence of new variants and their implications for transmissibility, severity, and immune evasion. The review highlights the important role of genomic surveillance in tracking viral mutations linked to establishing public health interventions. By analyzing the origins, global spread, and genetic evolution of SARS-CoV-2, valuable insights can be gained for the development of effective control measures, improvement of pandemic preparedness, and addressing future emerging infectious diseases of international concern.
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Affiliation(s)
- Marta Giovanetti
- Instituto Rene Rachou Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brazil
- Sciences and Technologies for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Italy, Rome, Italy
| | - Francesco Branda
- Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Rende, Italy
| | - Eleonora Cella
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, Florida, USA
| | - Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Liliana Bazzani
- Sciences and Technologies for Sustainable Development and One Health, Università Campus Bio-Medico di Roma, Italy, Rome, Italy
| | - Alessandra Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy
| | - Svetoslav Nanev Slavov
- Butantan Institute, São Paulo, Brazil
- Blood Center of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Domenico Benvenuto
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Marco Casu
- Department of Veterinary Medicine, University of Sassari, Sassari, Italy
| | - Luciane Amorim Santos
- Escola Bahiana de Medicina e Saúde Pública, Salvador, Bahia, Brazil
- Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Programa de Pós-graduação em Ciências da Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Praça Ramos de Queirós, s/n, Largo do Terreiro de Jesus, Salvador, Bahia, Brazil
| | - Alessia Lai
- Department of Biomedical and Clinical Sciences, L. Sacco Hospital, University of Milan, Milan, Italy
| | - Giangluglielmo Zehender
- Department of Biomedical and Clinical Sciences, L. Sacco Hospital, University of Milan, Milan, Italy
| | - Francesca Caccuri
- Section of Microbiology Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Andrea Ianni
- M.G. Vannini Hospital IFSC Rome, Research Unit in Hygiene UCBM Rome, Rome, Italy
| | - Arnaldo Caruso
- Section of Microbiology Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Stefano Pascarella
- Department of Biochemical Sciences "A. Rossi Fanelli", Sapienza University of Rome, Rome, Italy
| | | | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Rome, Italy
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8
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Gaythorpe KAM, Fitzjohn RG, Hinsley W, Imai N, Knock ES, Perez Guzman PN, Djaafara B, Fraser K, Baguelin M, Ferguson NM. Data pipelines in a public health emergency: The human in the machine. Epidemics 2023; 43:100676. [PMID: 36913804 DOI: 10.1016/j.epidem.2023.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 01/31/2023] [Accepted: 03/06/2023] [Indexed: 03/10/2023] Open
Abstract
In an emergency epidemic response, data providers supply data on a best-faith effort to modellers and analysts who are typically the end user of data collected for other primary purposes such as to inform patient care. Thus, modellers who analyse secondary data have limited ability to influence what is captured. During an emergency response, models themselves are often under constant development and require both stability in their data inputs and flexibility to incorporate new inputs as novel data sources become available. This dynamic landscape is challenging to work with. Here we outline a data pipeline used in the ongoing COVID-19 response in the UK that aims to address these issues. A data pipeline is a sequence of steps to carry the raw data through to a processed and useable model input, along with the appropriate metadata and context. In ours, each data type had an individual processing report, designed to produce outputs that could be easily combined and used downstream. Automated checks were in-built and added as new pathologies emerged. These cleaned outputs were collated at different geographic levels to provide standardised datasets. Finally, a human validation step was an essential component of the analysis pathway and permitted more nuanced issues to be captured. This framework allowed the pipeline to grow in complexity and volume and facilitated the diverse range of modelling approaches employed by researchers. Additionally, every report or modelling output could be traced back to the specific data version that informed it ensuring reproducibility of results. Our approach has been used to facilitate fast-paced analysis and has evolved over time. Our framework and its aspirations are applicable to many settings beyond COVID-19 data, for example for other outbreaks such as Ebola, or where routine and regular analyses are required.
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Affiliation(s)
- Katy A M Gaythorpe
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom.
| | - Rich G Fitzjohn
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Wes Hinsley
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Natsuko Imai
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Edward S Knock
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Pablo N Perez Guzman
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Bimandra Djaafara
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Keith Fraser
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Marc Baguelin
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
| | - Neil M Ferguson
- School of Public Health, Imperial College London, Praed Street, London, United Kingdom
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9
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Wang Z, Liu XF, Du Z, Wang L, Wu Y, Holme P, Lachmann M, Lin H, Wong ZSY, Xu XK, Sun Y. Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model. iScience 2022; 25:105079. [PMID: 36093379 PMCID: PMC9441477 DOI: 10.1016/j.isci.2022.105079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 07/04/2022] [Accepted: 08/31/2022] [Indexed: 11/26/2022] Open
Abstract
Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation. We propose a method to obtain epidemiological information from COVID-19 case reports The extracted information has 80% matching rate with the gold-standard manual coding We provide an online platform that can analyze epidemiological statistics in real time
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Affiliation(s)
- Zhizheng Wang
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Xiao Fan Liu
- Web Mining Laboratory, Department of Media and Communication, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK
| | - Ye Wu
- Computational Communication Research Center and School of Journalism and Communication, Beijing Normal University, Beijing, China
| | - Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
| | | | - Hongfei Lin
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
| | - Zoie S Y Wong
- Graduate School of Public Health, St. Luke's International University, Tokyo, Japan
| | - Xiao-Ke Xu
- College of Information and Communication Engineering, Dalian Minzu University, Liaoning, China
| | - Yuanyuan Sun
- College of Computer Science and Technology, Dalian University of Technology, Haishan Building No.2 Linggong Road, Dalian, Liaoning 116023, China
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10
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A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices. Comput Biol Med 2022; 149:106070. [PMID: 36099862 PMCID: PMC9433350 DOI: 10.1016/j.compbiomed.2022.106070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 08/03/2022] [Accepted: 08/27/2022] [Indexed: 11/30/2022]
Abstract
Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.
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11
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Kryukov K, Jin L, Nakagawa S. Efficient compression of SARS-CoV-2 genome data using Nucleotide Archival Format. PATTERNS 2022; 3:100562. [PMID: 35818472 PMCID: PMC9259476 DOI: 10.1016/j.patter.2022.100562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Gutierrez B, Castelán Sánchez HG, Candido DDS, Jackson B, Fleishon S, Houzet R, Ruis C, Delaye L, Faria NR, Rambaut A, Pybus OG, Escalera-Zamudio M. Emergence and widespread circulation of a recombinant SARS-CoV-2 lineage in North America. Cell Host Microbe 2022; 30:1112-1123.e3. [PMID: 35853454 PMCID: PMC9212848 DOI: 10.1016/j.chom.2022.06.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/18/2022] [Accepted: 06/16/2022] [Indexed: 02/09/2023]
Abstract
Although recombination is a feature of coronavirus evolution, previously detected recombinant lineages of SARS-CoV-2 have shown limited circulation thus far. Here, we present a detailed phylogenetic analysis of four SARS-CoV-2 lineages to investigate the possibility of virus recombination among them. Our analyses reveal well-supported phylogenetic differences between the Orf1ab region encoding viral non-structural proteins and the rest of the genome, including Spike (S) protein and remaining reading frames. By accounting for several deletions in NSP6, Orf3a, and S, we conclude that the B.1.628 major cluster, now designated as lineage XB, originated from a recombination event between viruses of B.1.631 and B.1.634 lineages. This scenario is supported by the spatiotemporal distribution of these lineages across the USA and Mexico during 2021, suggesting that the recombination event originated in this geographical region. This event raises important questions regarding the role and potential effects of recombination on SARS-CoV-2 evolution.
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Affiliation(s)
- Bernardo Gutierrez
- Department of Zoology, University of Oxford, Oxford, UK; Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), México; Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito USFQ, Quito, Ecuador.
| | - Hugo G Castelán Sánchez
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), México; Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México
| | - Darlan da Silva Candido
- Department of Zoology, University of Oxford, Oxford, UK; Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Ben Jackson
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | | | | | - Christopher Ruis
- Molecular Immunity Unit, Department of Medicine, University of Cambridge, Cambridge, UK; Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Luis Delaye
- Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), México; Departamento de Ingeniería Genética, Unidad Irapuato, CINVESTAV, Irapuato, Mexico
| | - Nuno R Faria
- Department of Zoology, University of Oxford, Oxford, UK; Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil; MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK; The Abdul Latif Jameel Institute for Disease and Emergency Analytics, School of Public Health, Imperial College London, London, UK
| | - Andrew Rambaut
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Oliver G Pybus
- Department of Zoology, University of Oxford, Oxford, UK; Department of Pathobiology, Royal Veterinary College, London, UK.
| | - Marina Escalera-Zamudio
- Department of Zoology, University of Oxford, Oxford, UK; Consorcio Mexicano de Vigilancia Genómica (CoViGen-Mex), México.
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13
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Kraemer MUG, Tegally H, Pigott DM, Dasgupta A, Sheldon J, Wilkinson E, Schultheiss M, Han A, Oglia M, Marks S, Kanner J, O'Brien K, Dandamudi S, Rader B, Sewalk K, Bento AI, Scarpino SV, de Oliveira T, Bogoch II, Katz R, Brownstein JS. Tracking the 2022 monkeypox outbreak with epidemiological data in real-time. THE LANCET. INFECTIOUS DISEASES 2022; 22:941-942. [PMID: 35690074 PMCID: PMC9629664 DOI: 10.1016/s1473-3099(22)00359-0] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 05/27/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Moritz U G Kraemer
- Department of Biology, University of Oxford, Oxford OX1 3SY, UK; Pandemic Sciences Institute, University of Oxford, Oxford OX1 3SY, UK.
| | - Houriiyah Tegally
- Centre for Epidemic Response and Innovation, School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, DC, USA
| | - Abhishek Dasgupta
- Department of Biology, University of Oxford, Oxford OX1 3SY, UK; Department of Computer Science, University of Oxford, Oxford OX1 3SY, UK
| | - James Sheldon
- The Roux Institute, Northeastern University, Portland, ME, USA
| | - Eduan Wilkinson
- Centre for Epidemic Response and Innovation, School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | | | - Aimee Han
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Mark Oglia
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Spencer Marks
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Joshua Kanner
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Katelynn O'Brien
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Sudheer Dandamudi
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Benjamin Rader
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Kara Sewalk
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Ana I Bento
- School of Public Health, Indiana University, Bloomington, IN, USA; Pandemic Prevention Institute, The Rockefeller Foundation, New York, NY, USA
| | - Samuel V Scarpino
- The Roux Institute, Northeastern University, Portland, ME, USA; Pandemic Prevention Institute, The Rockefeller Foundation, New York, NY, USA; Santa Fe Institute, Santa Fe, NM, USA
| | - Tulio de Oliveira
- Centre for Epidemic Response and Innovation, School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa
| | - Isaac I Bogoch
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - John S Brownstein
- Boston Children's Hospital, Harvard Medical School, Boston, MA 02215, USA.
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14
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OpenStreetMap Contribution to Local Data Ecosystems in COVID-19 Times: Experiences and Reflections from the Italian Case. DATA 2022. [DOI: 10.3390/data7040039] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Data and digital technologies have been at the core of the societal response to COVID-19 since the beginning of the pandemic. This work focuses on the specific contribution of the OpenStreetMap (OSM) project to address the early stage of the COVID-19 crisis (approximately from February to May 2020) in Italy. Several activities initiated by the Italian OSM community are described, including: mapping ‘red zones’ (the first municipalities affected by the emergency); updating OSM pharmacies based on the authoritative dataset from the Ministry of Health; adding information on delivery services of commercial activities during COVID-19 times; publishing web maps to offer COVID-19-specific information at the local level; and developing software tools to help collect new data. Those initiatives are analysed from a data ecosystem perspective, identifying the actors, data and data flows involved, and reflecting on the enablers and barriers for their success from a technical, organisational and legal point of view. The OSM project itself is then assessed in the wider European policy context, in particular against the objectives of the recent European strategy for data, highlighting opportunities and challenges for scaling successful approaches such as those to fight COVID-19 from the local to the national and European scales.
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15
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Guidotti E. A worldwide epidemiological database for COVID-19 at fine-grained spatial resolution. Sci Data 2022; 9:112. [PMID: 35351921 PMCID: PMC8964767 DOI: 10.1038/s41597-022-01245-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/07/2022] [Indexed: 11/08/2022] Open
Abstract
This database provides the daily time-series of COVID-19 cases, deaths, recovered people, tests, vaccinations, and hospitalizations, for more than 230 countries, 760 regions, and 12,000 lower-level administrative divisions. The geographical entities are associated with identifiers to match with hydrometeorological, geospatial, and mobility data. The database includes policy measures at the national and, when available, sub-national levels. The data acquisition pipeline is open-source and fully automated. As most governments revise the data retrospectively, the database always updates the complete time-series to mirror the original source. Vintage data, immutable snapshots of the data taken each day, are provided to ensure research reproducibility. The latest data are updated on an hourly basis, and the vintage data are available since April 14, 2020. All the data are available in CSV files or SQLite format. By unifying the access to the data, this work makes it possible to study the pandemic on a global scale with high resolution, taking into account within-country variations, nonpharmaceutical interventions, and environmental and exogenous variables.
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Affiliation(s)
- Emanuele Guidotti
- University of Neuchâtel, Institute of Financial Analysis, Neuchâtel, 2000, Switzerland.
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16
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Astley CM, Tuli G, Mc Cord KA, Cohn EL, Rader B, Varrelman TJ, Chiu SL, Deng X, Stewart K, Farag TH, Barkume KM, LaRocca S, Morris KA, Kreuter F, Brownstein JS. Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base. Proc Natl Acad Sci U S A 2021; 118:e2111455118. [PMID: 34903657 PMCID: PMC8713788 DOI: 10.1073/pnas.2111455118] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2021] [Indexed: 11/18/2022] Open
Abstract
Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.
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Affiliation(s)
- Christina M Astley
- Division of Endocrinology, Boston Children's Hospital, Boston, MA 02115;
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
- Harvard Medical School, Boston, MA 02115
- Broad Institute of Harvard and MIT, Cambridge, MA 02142
| | - Gaurav Tuli
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
| | - Kimberly A Mc Cord
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
| | - Emily L Cohn
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
| | - Benjamin Rader
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
- Department of Epidemiology, Boston University, Boston, MA 02118
| | - Tanner J Varrelman
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
| | - Samantha L Chiu
- Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742
| | - Xiaoyi Deng
- Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742
| | - Kathleen Stewart
- Center for Geospatial Information Science, University of Maryland, College Park, MD 20742
| | | | | | | | | | - Frauke Kreuter
- Joint Program in Survey Methodology, University of Maryland, College Park, MD 20742
- Department of Statistics, Ludwig-Maximilians-Universität, Munich 80539, Germany
| | - John S Brownstein
- Computational Epidemiology Lab, Boston Children's Hospital, Boston, MA 02115
- Harvard Medical School, Boston, MA 02115
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17
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Nevin J, Lees M, Groth P. The non-linear impact of data handling on network diffusion models. PATTERNS (NEW YORK, N.Y.) 2021; 2:100397. [PMID: 34950910 PMCID: PMC8672192 DOI: 10.1016/j.patter.2021.100397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/23/2021] [Accepted: 11/02/2021] [Indexed: 12/04/2022]
Abstract
Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the challenge of errors in data collection have been analyzed in the literature, here we highlight the importance of data handling in the modeling and simulation process, and how particular data handling errors can lead to errors in model output. We develop a framework for assessing the impact of potential data errors for models of spreading processes on networks, a broad class of models that capture many important real-world phenomena (e.g., epidemics, rumor spread, etc.). We focus on the susceptible-infected-removed (SIR) and Threshold models and examine how systematic errors in data handling impact the predicted spread of a virus (or information). Our results demonstrate that data handling errors can have significant impact on model conclusions especially in critical regions of a system.
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Affiliation(s)
- James Nevin
- University of Amsterdam, Amsterdam, the Netherlands
| | - Michael Lees
- University of Amsterdam, Amsterdam, the Netherlands
| | - Paul Groth
- University of Amsterdam, Amsterdam, the Netherlands
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18
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García-Cremades S, Morales-García J, Hernández-Sanjaime R, Martínez-España R, Bueno-Crespo A, Hernández-Orallo E, López-Espín JJ, Cecilia JM. Improving prediction of COVID-19 evolution by fusing epidemiological and mobility data. Sci Rep 2021; 11:15173. [PMID: 34312455 PMCID: PMC8313557 DOI: 10.1038/s41598-021-94696-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/09/2021] [Indexed: 02/07/2023] Open
Abstract
We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period, since they involve the closure of economic activities such as tourism, cultural activities, or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. However, the collapse of the health system and the unpredictability of human behavior, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create a decision support system for policy-makers. We consider a wide branch of models including artificial neural networks such as LSTM and GRU and statistically based models such as autoregressive (AR) or ARIMA. Moreover, several consensus strategies to ensemble all models into one system are proposed to obtain better results in this uncertain environment. Finally, a multivariate model that includes mobility data provided by Google is proposed to better forecast trend changes in the 14-day CI. A real case study in Spain is evaluated, providing very accurate results for the prediction of 14-day CI in scenarios with and without trend changes, reaching 0.93 [Formula: see text], 4.16 RMSE and 1.08 MAE.
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Affiliation(s)
- Santi García-Cremades
- Center of Operations Research, Miguel Hernandez University of Elche (UMH), 03202, Elche, Spain
| | - Juan Morales-García
- Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain
| | | | - Raquel Martínez-España
- Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain
| | - Andrés Bueno-Crespo
- Computer Science Department, Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain
| | - Enrique Hernández-Orallo
- Computer Engineering Department, Univesitat Politècnica de València (UPV), 46022, Valencia, Spain
| | - José J López-Espín
- Center of Operations Research, Miguel Hernandez University of Elche (UMH), 03202, Elche, Spain
| | - José M Cecilia
- Computer Engineering Department, Univesitat Politècnica de València (UPV), 46022, Valencia, Spain.
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19
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Hay JA, Kennedy-Shaffer L, Kanjilal S, Lennon NJ, Gabriel SB, Lipsitch M, Mina MJ. Estimating epidemiologic dynamics from cross-sectional viral load distributions. Science 2021; 373:eabh0635. [PMID: 34083451 PMCID: PMC8527857 DOI: 10.1126/science.abh0635] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022]
Abstract
Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but case data used for such estimation are confounded by variable testing practices. We show that the population distribution of viral loads observed under random or symptom-based surveillance-in the form of cycle threshold (Ct) values obtained from reverse transcription quantitative polymerase chain reaction testing-changes during an epidemic. Thus, Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining data from multiple such samples improves the precision and robustness of this estimation. We apply our methods to Ct values from surveillance conducted during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in a variety of settings and offer alternative approaches for real-time estimates of epidemic trajectories for outbreak management and response.
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Affiliation(s)
- James A Hay
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lee Kennedy-Shaffer
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA
| | - Sanjat Kanjilal
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | - Marc Lipsitch
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michael J Mina
- Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
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20
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Fang L, Wang X. COVID-19 deep classification network based on convolution and deconvolution local enhancement. Comput Biol Med 2021; 135:104588. [PMID: 34182330 PMCID: PMC8216864 DOI: 10.1016/j.compbiomed.2021.104588] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/22/2021] [Accepted: 06/16/2021] [Indexed: 12/11/2022]
Abstract
Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.
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Affiliation(s)
- Lingling Fang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China.
| | - Xin Wang
- Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China
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21
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Cortés U, Cortés A, Garcia-Gasulla D, Pérez-Arnal R, Álvarez-Napagao S, Àlvarez E. The ethical use of high-performance computing and artificial intelligence: fighting COVID-19 at Barcelona Supercomputing Center. AI AND ETHICS 2021; 2:325-340. [PMID: 34790948 PMCID: PMC8101339 DOI: 10.1007/s43681-021-00056-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 04/15/2021] [Indexed: 10/24/2022]
Abstract
The COVID-19 pandemic has created an extraordinary medical, economic and humanitarian emergency. Artificial intelligence, in combination with other digital technologies, is being used as a tool to support the fight against the viral pandemic that has affected the entire world since the beginning of 2020. Barcelona Supercomputing Center collaborates in the battle against the coronavirus in different areas: the application of bioinformatics for the research on the virus and its possible treatments, the use of artificial intelligence, natural language processing and big data techniques to analyse the spread and impact of the pandemic, and the use of the MareNostrum 4 supercomputer to enable massive analysis on COVID-19 data. Many of these activities have included the use of personal and sensitive data of citizens, which, even during a pandemic, should be treated and handled with care. In this work we discuss our approach based on an ethical, transparent and fair use of this information, an approach aligned with the guidelines proposed by the European Union.
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Affiliation(s)
- Ulises Cortés
- Universitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Atia Cortés
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Dario Garcia-Gasulla
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Raquel Pérez-Arnal
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Sergio Álvarez-Napagao
- Barcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain
| | - Enric Àlvarez
- Universitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain
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