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Aksenova A, Johny A, Adams T, Gribbon P, Jacobs M, Hofmann-Apitius M. Current state of data stewardship tools in life science. Front Big Data 2024; 7:1428568. [PMID: 39351001 PMCID: PMC11439729 DOI: 10.3389/fdata.2024.1428568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 08/23/2024] [Indexed: 10/04/2024] Open
Abstract
In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.
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Affiliation(s)
- Anna Aksenova
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Anoop Johny
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Tim Adams
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Phil Gribbon
- Fraunhofer Institute for Translational Medicine and Pharmacology, Discovery Research Screening Port, Hamburg, Germany
| | - Marc Jacobs
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Martin Hofmann-Apitius
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
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2
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Gierend K, Krüger F, Genehr S, Hartmann F, Siegel F, Waltemath D, Ganslandt T, Zeleke AA. Provenance Information for Biomedical Data and Workflows: Scoping Review. J Med Internet Res 2024; 26:e51297. [PMID: 39178413 PMCID: PMC11380065 DOI: 10.2196/51297] [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: 07/27/2023] [Revised: 05/30/2024] [Accepted: 06/17/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. OBJECTIVE This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. METHODS This scoping review followed the methodological framework developed by Arksey and O'Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. RESULTS The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. CONCLUSIONS As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/31750.
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Affiliation(s)
- Kerstin Gierend
- Department of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank Krüger
- Faculty of Engineering, Wismar University of Applied Sciences, Wismar, Germany
- Institute of Communications Engineering, University of Rostock, Rostock, Germany
| | - Sascha Genehr
- Institute of Communications Engineering, University of Rostock, Rostock, Germany
| | - Francisca Hartmann
- Department of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Fabian Siegel
- Department of Biomedical Informatics, Mannheim Institute for intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dagmar Waltemath
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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3
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Wilsdorf P, Reinhardt O, Prike T, Hinsch M, Bijak J, Uhrmacher AM. Simulation studies of social systems: telling the story based on provenance patterns. ROYAL SOCIETY OPEN SCIENCE 2024; 11:240258. [PMID: 39113768 PMCID: PMC11304336 DOI: 10.1098/rsos.240258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 08/10/2024]
Abstract
Social simulation studies are complex. They typically combine various data sources and hypotheses about the system's mechanisms that are integrated by intertwined processes of model building, simulation experiment execution and analysis. Various documentation approaches exist to increase the transparency and traceability of complex social simulation studies. Provenance standards enable the formalization of information on sources and activities, which contribute to the generation of an entity, in a queryable and computationally accessible manner. Provenance patterns can be defined as constraints on the relationships between specific types of activities and entities of a simulation study. In this paper, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analysing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of an agent-based simulation project and by presenting its implementation in a web-based tool.
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Affiliation(s)
- Pia Wilsdorf
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Oliver Reinhardt
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Toby Prike
- School of Psychological Science, The University of Western Australia, Perth, Australia
| | - Martin Hinsch
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Jakub Bijak
- Department of Social Statistics and Demography, University of Southampton, Southampton, UK
| | - Adelinde M. Uhrmacher
- Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany
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4
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Bai J, Mosbach S, Taylor CJ, Karan D, Lee KF, Rihm SD, Akroyd J, Lapkin AA, Kraft M. A dynamic knowledge graph approach to distributed self-driving laboratories. Nat Commun 2024; 15:462. [PMID: 38263405 PMCID: PMC10805810 DOI: 10.1038/s41467-023-44599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist's research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.
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Affiliation(s)
- Jiaru Bai
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
| | - Sebastian Mosbach
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Connor J Taylor
- Astex Pharmaceuticals, 436 Cambridge Science Park Milton Road, Cambridge, CB4 0QA, UK
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- Faculty of Engineering, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Dogancan Karan
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Kok Foong Lee
- CMCL Innovations, Sheraton House, Cambridge, CB3 0AX, UK
| | - Simon D Rihm
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Jethro Akroyd
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore
- Innovation Centre in Digital Molecular Technologies, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Markus Kraft
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK.
- Cambridge Centre for Advanced Research and Education in Singapore (CARES), 1 Create Way, CREATE Tower, #05-05, Singapore, 138602, Singapore.
- School of Chemical and Biomedical Engineering, Nanyang Technological University, 62 Nanyang Drive, 637459, Singapore, Singapore.
- The Alan Turing Institute, London, NW1 2DB, UK.
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Jean-Quartier C, Stryeck S, Thien A, Vrella B, Kleinschuster J, Spreitzer E, Wali M, Mueller H, Holzinger A, Jeanquartier F. Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences. Digit Health 2024; 10:20552076241271769. [PMID: 39281045 PMCID: PMC11394355 DOI: 10.1177/20552076241271769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/19/2024] [Indexed: 09/18/2024] Open
Abstract
Objective Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience. Methods We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted. Results The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users. Conclusion The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.
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Affiliation(s)
- Claire Jean-Quartier
- Research Data Management, Graz University of Technology, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
| | - Sarah Stryeck
- Research Center Pharmaceutical Engineering GmbH, Graz, Austria
| | - Alexander Thien
- Institute of Technical Informatics, Graz University of Technology, Graz, Austria
| | - Burim Vrella
- Institute of Technical Informatics, Graz University of Technology, Graz, Austria
| | | | - Emil Spreitzer
- Division of Molecular Biology and Biochemistry, Medical University Graz, Austria
| | - Mojib Wali
- Research Data Management, Graz University of Technology, Graz, Austria
| | - Heimo Mueller
- Information Science and Machine Learning Group, Diagnostic and Research Center for Molecular Biomedicine, Medical University Graz, Austria
| | - Andreas Holzinger
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
- Human-Centered AI Lab, Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna, Austria
- Institute of Interactive Systems and Data Science, Graz University of Technology, Graz, Austria
| | - Fleur Jeanquartier
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria
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Gierend K, Waltemath D, Ganslandt T, Siegel F. Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study. JMIR Form Res 2023; 7:e50027. [PMID: 38060305 PMCID: PMC10739241 DOI: 10.2196/50027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/25/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. OBJECTIVE The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. METHODS We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. RESULTS This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. CONCLUSIONS The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.
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Affiliation(s)
- Kerstin Gierend
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center and Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Ganslandt
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Fabian Siegel
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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7
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Pooley CM, Doeschl-Wilson AB, Marion G. Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210298. [PMID: 35965466 PMCID: PMC9376725 DOI: 10.1098/rsta.2021.0298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 03/10/2022] [Indexed: 05/08/2023]
Abstract
Well parameterized epidemiological models including accurate representation of contacts are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here, we fit age-stratified models, including re-estimation of relative contact rates between age classes, to public data describing the 2020-2021 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing approximate Bayesian computation (ABC) methodology with model-based proposals (MBPs) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalization rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrization of dynamic transmission models that can inform data-driven public health policy and interventions. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- Christopher M. Pooley
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
| | | | - Glenn Marion
- Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Peter Guthrie Tait Road, Edinburgh EH9 3FD, UK
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8
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Panovska-Griffiths J, Waites W, Ackland GJ. Technical challenges of modelling real-life epidemics and examples of overcoming these. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20220179. [PMID: 35965472 PMCID: PMC9376714 DOI: 10.1098/rsta.2022.0179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- J. Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- The Queen’s College, University of Oxford, Oxford, UK
| | - W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK
| | - G. J. Ackland
- Institute of Condensed Matter and Complex Systems, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK
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9
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Dykes J, Abdul-Rahman A, Archambault D, Bach B, Borgo R, Chen M, Enright J, Fang H, Firat EE, Freeman E, Gönen T, Harris C, Jianu R, John NW, Khan S, Lahiff A, Laramee RS, Matthews L, Mohr S, Nguyen PH, Rahat AAM, Reeve R, Ritsos PD, Roberts JC, Slingsby A, Swallow B, Torsney-Weir T, Turkay C, Turner R, Vidal FP, Wang Q, Wood J, Xu K. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210299. [PMID: 35965467 PMCID: PMC9376715 DOI: 10.1098/rsta.2021.0299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
| | | | | | | | | | - Min Chen
- University of Oxford, Oxford, UK
| | | | - Hui Fang
- Loughborough University, Loughborough, UK
| | | | | | | | - Claire Harris
- Biomathematics and Statistics Scotland, Edinburgh, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Qiru Wang
- University of Nottingham, Nottingham, UK
| | - Jo Wood
- City, University of London, London, UK
| | - Kai Xu
- Middlesex University, London, UK
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10
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Ackland GJ, Panovska-Griffiths J, Waites W, Cates ME. The Royal Society RAMP modelling initiative. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210316. [PMID: 35965460 PMCID: PMC9376713 DOI: 10.1098/rsta.2021.0316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 07/05/2022] [Indexed: 05/07/2023]
Abstract
Normally, science proceeds following a well-established set of principles. Studies are done with an emphasis on correctness, are submitted to a journal editor who evaluates their relevance, and then undergo anonymous peer review by experts before publication in a journal and acceptance by the scientific community via the open literature. This process is slow, but its accuracy has served all fields of science well. In an emergency situation, different priorities come to the fore. Research and review need to be conducted quickly, and the target audience consists of policymakers. Scientists must jostle for the attention of non-specialists without sacrificing rigour, and must deal not only with peer assessment but also with media scrutiny by journalists who may have agendas other than ensuring scientific correctness. Here, we describe how the Royal Society coordinated efforts of diverse scientists to help model the coronavirus epidemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
- G. J. Ackland
- Institute of Condensed Matter and Complex Systems, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK
| | - J. Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX1 4AW, UK
- The Queen’s College, University of Oxford, Oxford OX1 4AW, UK
| | - W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow G1 1XH, UK
| | - M. E. Cates
- DAMTP, University of Cambridge, Cambridge CB3 0WA, UK
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11
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Dykes J, Abdul-Rahman A, Archambault D, Bach B, Borgo R, Chen M, Enright J, Fang H, Firat EE, Freeman E, Gönen T, Harris C, Jianu R, John NW, Khan S, Lahiff A, Laramee RS, Matthews L, Mohr S, Nguyen PH, Rahat AAM, Reeve R, Ritsos PD, Roberts JC, Slingsby A, Swallow B, Torsney-Weir T, Turkay C, Turner R, Vidal FP, Wang Q, Wood J, Xu K. Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022. [PMID: 35965467 DOI: 10.6084/m9.figshare.c.6080807] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
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Affiliation(s)
| | | | | | | | | | - Min Chen
- University of Oxford, Oxford, UK
| | | | - Hui Fang
- Loughborough University, Loughborough, UK
| | | | | | | | - Claire Harris
- Biomathematics and Statistics Scotland, Edinburgh, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Qiru Wang
- University of Nottingham, Nottingham, UK
| | - Jo Wood
- City, University of London, London, UK
| | - Kai Xu
- Middlesex University, London, UK
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Marion G, Hadley L, Isham V, Mollison D, Panovska-Griffiths J, Pellis L, Tomba GS, Scarabel F, Swallow B, Trapman P, Villela D. Modelling: Understanding pandemics and how to control them. Epidemics 2022; 39:100588. [PMID: 35679714 DOI: 10.1016/j.epidem.2022.100588] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 03/22/2022] [Accepted: 05/26/2022] [Indexed: 12/11/2022] Open
Abstract
New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation and analysis of epidemic models. Innovations in modelling can lead to new insights into epidemic processes and better use of available data, yielding improved disease control and stimulating collection of better data and new data types. Here we identify key challenges for the structure, formulation, analysis and use of mathematical models of pathogen transmission relevant to current and future pandemics.
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Affiliation(s)
- Glenn Marion
- Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK.
| | - Liza Hadley
- Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, UK
| | - Valerie Isham
- Department of Statistical Science, University College London, UK
| | - Denis Mollison
- Department of Actuarial Mathematics and Statistics, Heriot-Watt University, UK
| | - Jasmina Panovska-Griffiths
- The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; The Queen's College, Oxford University, UK
| | - Lorenzo Pellis
- Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK; Joint UNIversities Pandemic and Epidemiological Research, UK
| | | | - Francesca Scarabel
- Department of Mathematics, University of Manchester, UK; Joint UNIversities Pandemic and Epidemiological Research, UK; CDLab - Computational Dynamics Laboratory, Department of Mathematics, Computer Science and Physics, University of Udine, Italy
| | - Ben Swallow
- Scottish COVID-19 Response Consortium, UK; School of Mathematics and Statistics, University of Glasgow, UK
| | - Pieter Trapman
- Department of Mathematics, Stockholm University, Stockholm, Sweden
| | - Daniel Villela
- Program of Scientific Computing, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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