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Michalowski M, Rao M, Wilk S, Michalowski W, Carrier M. Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines. Artif Intell Med 2023; 140:102550. [PMID: 37210156 DOI: 10.1016/j.artmed.2023.102550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 05/22/2023]
Abstract
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.
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Affiliation(s)
| | | | - Szymon Wilk
- Poznan University of Technology, Poznan, Poland
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Waites W, Cavaliere M, Danos V, Datta R, Eggo RM, Hallett TB, Manheim D, Panovska-Griffiths J, Russell TW, Zarnitsyna VI. Compositional modelling of immune response and virus transmission dynamics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210307. [PMID: 35965463 PMCID: PMC9376723 DOI: 10.1098/rsta.2021.0307] [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: 05/02/2023]
Abstract
Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. 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)
- W. Waites
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK
| | - M. Cavaliere
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK
| | - V. Danos
- Département d’Informatique, École Normale Supérieure, Paris, France
| | - R. Datta
- Datta Enterprises LLC, San Francisco, CA, USA
| | - R. M. Eggo
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - T. B. Hallett
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - D. Manheim
- Technion, Israel Institute of Technology, Haifa, Israel
| | - 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
| | - T. W. Russell
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
| | - V. I. Zarnitsyna
- Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA
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Waites W, Pearson CAB, Gaskell KM, House T, Pellis L, Johnson M, Gould V, Hunt A, Stone NRH, Kasstan B, Chantler T, Lal S, Roberts CH, Goldblatt D, Marks M, Eggo RM. Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK. Sci Rep 2022; 12:8550. [PMID: 35595824 PMCID: PMC9121858 DOI: 10.1038/s41598-022-12517-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/12/2022] [Indexed: 11/22/2022] Open
Abstract
Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.
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Affiliation(s)
- William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK.
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland, UK.
| | - Carl A B Pearson
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Katherine M Gaskell
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Thomas House
- School of Mathematics, University of Manchester, Manchester, UK
| | - Lorenzo Pellis
- School of Mathematics, University of Manchester, Manchester, UK
| | - Marina Johnson
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Victoria Gould
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Adam Hunt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Neil R H Stone
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Ben Kasstan
- Centre for Health, Law and Society, University of Bristol Law School, Bristol, UK
- Department of Sociology and Anthropology, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Tracey Chantler
- Department of Global Health and Development, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Sham Lal
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - Chrissy H Roberts
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
| | - David Goldblatt
- Great Ormond Street Institute of Child Health Biomedical Research Centre, University College London, London, UK
| | - Michael Marks
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
- Hospital for Tropical Diseases, University College London Hospital NHS Foundation Trust, London, UK
| | - Rosalind M Eggo
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, UK
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Karr J, Malik-Sheriff RS, Osborne J, Gonzalez-Parra G, Forgoston E, Bowness R, Liu Y, Thompson R, Garira W, Barhak J, Rice J, Torres M, Dobrovolny HM, Tang T, Waites W, Glazier JA, Faeder JR, Kulesza A. Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:822606. [PMID: 36909847 PMCID: PMC10002468 DOI: 10.3389/fsysb.2022.822606] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.
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Affiliation(s)
- Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Rahuman S. Malik-Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, United Kingdom
| | - James Osborne
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, Australia
| | | | - Eric Forgoston
- Department of Applied Mathematics and Statistics, Montclair State University, Montclair, NJ, United States
| | - Ruth Bowness
- Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
| | - Robin Thompson
- Mathematics Institute and the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Winston Garira
- Department of Mathematics and Applied Mathematics, Modelling Health and Environmental Linkages Research Group, University of Venda, Limpopo, South Africa
| | - Jacob Barhak
- Jacob Barhak Analytics, Austin, TX, United States
| | - John Rice
- Independent Retired Working Group Volunteer, Virginia Beach, VA, United States
| | - Marcella Torres
- Department of Mathematics and Computer Science, University of Richmond, Richmond, VA, United States
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, United States
| | - Tingting Tang
- Department of Mathematics and Statistics in San Diego State University (SDSU) and SDSU Imperial Valley, Calexico, CA, United States
| | - William Waites
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Department of Computer and Information Sciences, University of Strathclyde, Glasgow, Scotland
| | - James A. Glazier
- Biocomplexity Institute, Indiana University, Bloomington, IN, United States
| | - James R. Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United States
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