Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts
Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therap...
Saved in:
| Published in | BMC medical research methodology Vol. 23; no. 1; pp. 76 - 21 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
29.03.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2288 1471-2288 |
| DOI | 10.1186/s12874-023-01856-1 |
Cover
| Abstract | Background
COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.
Methods
In early 2020, we began developing such causal models. The SARS-CoV-2 virus’s rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.
Results
We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.
Conclusions
Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. |
|---|---|
| AbstractList | Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Methods In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. Results We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Conclusions Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. Keywords: COVID-19, Pathophysiology, Bayesian network, DAG, DBN, Causal model, Expert elicitation, Decision support, Experimental design COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Methods In early 2020, we began developing such causal models. The SARS-CoV-2 virus’s rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. Results We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Conclusions Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.BACKGROUNDCOVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.METHODSIn early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.RESULTSWe present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.CONCLUSIONSOur method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. BackgroundCOVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.MethodsIn early 2020, we began developing such causal models. The SARS-CoV-2 virus’s rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.ResultsWe present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.ConclusionsOur method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. Abstract Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Methods In early 2020, we began developing such causal models. The SARS-CoV-2 virus’s rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. Results We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. Conclusions Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases. |
| ArticleNumber | 76 |
| Audience | Academic |
| Author | Nyberg, Erik P. Mascaro, Steven Wu, Yue Ramsay, Jessica A. Snelling, Thomas L. Woodberry, Owen Pearson, Ross Mace, Ariel O. Nicholson, Ann E. Foley, David A. |
| Author_xml | – sequence: 1 givenname: Steven surname: Mascaro fullname: Mascaro, Steven organization: Faculty of Information Technology, Monash University, Bayesian Intelligence Pty Ltd – sequence: 2 givenname: Yue surname: Wu fullname: Wu, Yue organization: School of Public Health, University of Sydney – sequence: 3 givenname: Owen surname: Woodberry fullname: Woodberry, Owen organization: Faculty of Information Technology, Monash University, Bayesian Intelligence Pty Ltd – sequence: 4 givenname: Erik P. surname: Nyberg fullname: Nyberg, Erik P. organization: Faculty of Information Technology, Monash University – sequence: 5 givenname: Ross surname: Pearson fullname: Pearson, Ross organization: Faculty of Information Technology, Monash University – sequence: 6 givenname: Jessica A. surname: Ramsay fullname: Ramsay, Jessica A. organization: Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia – sequence: 7 givenname: Ariel O. surname: Mace fullname: Mace, Ariel O. organization: Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Department of General Paediatrics, Perth Children’s Hospital, Department of Paediatrics, Fiona Stanley Hospital – sequence: 8 givenname: David A. surname: Foley fullname: Foley, David A. organization: Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Microbiology, PathWest Laboratory Medicine – sequence: 9 givenname: Thomas L. surname: Snelling fullname: Snelling, Thomas L. organization: School of Public Health, University of Sydney, Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, School of Public Health, Curtin University, Menzies School of Health Research, Charles Darwin University – sequence: 10 givenname: Ann E. surname: Nicholson fullname: Nicholson, Ann E. email: ann.nicholson@monash.edu organization: Faculty of Information Technology, Monash University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36991342$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUk1v1DAQtVARbRf-AAcUiQuXFI-dOM4JtcvXSkW9AFfLcSaLl8Re7ISy_x63u_3YClUoB0fj99543ptjcuC8Q0JeAj0BkOJtBCarIqeM5xRkKXJ4Qo6gqCBnTMqDe_-H5DjGFaVQSS6ekUMu6hp4wY7I6otvsbdumc0vvi_e51BnrY2oI2br4A3GiDFrNlnAwY-YJaixox6td5nvMqOnqPvsTG8wWu0yh-OlDz9j1gU_ZAO21qRr_LPGMMbn5Gmn-4gvdueMfPv44ev8c35-8WkxPz3PjaByzFEXTIi6LZBJ0DUVYEpdCsqhkV0DnBtmOErOQXSpxKCisuxkIw0UvKCCz8hiq9t6vVLrYAcdNsprq64LPiyVDqM1PSpsaMs7rClSLATXjdEdrSrBSxCs1Dpp8a3W5NZ6c6n7_lYQqLpKQW1TUCkFdZ2CgsR6t2WtpyaZYNCNQfd7T9m_cfaHWvrfSZOWVKYZZ-TNTiH4XxPGUQ02Gux77dBPUbGqZnVql2yZkdcPoCs_BZccVkxSTgGAlneopU5zW9f51NhciarTquCC0ooWCXXyD1T6WhysSdvX2VTfI7y6P-ntiDcLlgBsCzDBxxiw-z__5APSzdKl59j-ceousJj6uCWGOzceYf0F1jgBsQ |
| CitedBy_id | crossref_primary_10_1016_j_kint_2023_07_013 crossref_primary_10_1038_s41541_024_00955_4 crossref_primary_10_1038_s41598_024_59236_8 |
| Cites_doi | 10.1016/0040-1625(91)90039-I 10.1287/opre.7.5.646 10.1080/13669877.2020.1756381 10.1287/isre.1050.0051 10.1002/mma.6345 10.1257/jep.26.3.157 10.1101/2020.02.20.20023572 10.1016/S2468-2667(21)00002-5 10.1145/3322640.3326716 10.1111/j.1539-6924.2009.01337.x 10.1002/9780470561119.socpsy002032 10.1111/2041-210X.12857 10.1016/j.ssci.2016.03.001 10.1186/s12931-020-01462-5 10.1017/S0950268821001357 10.1017/CBO9781139046855.020 10.1037/a0022734 10.1037/0033-295X.112.2.494 10.1101/2020.04.10.20060426 10.1002/qre.410 10.1037/0278-7393.30.2.299 10.1016/j.ress.2005.11.037 10.1093/ije/dyaa213 10.1186/s40249-020-00683-6 10.1007/s10506-016-9183-4 10.1371/journal.pone.0183580 10.1109/69.868902 10.1111/j.1467-9280.2009.02333.x 10.1002/wcs.1184 10.5840/jphil20091061128 10.24963/ijcai.2017/202 10.1109/2.59 10.1007/11941439_23 10.1002/j.2162-6057.2006.tb01267.x 10.1111/risa.13759 10.5694/mja2.50606 10.12688/wellcomeopenres.15881.1 10.1016/j.envsoft.2016.04.012 10.1136/medethics-2020-106494 10.1038/s41467-020-17419-7 10.1016/S1473-3099(20)30144-4 |
| ContentType | Journal Article |
| Contributor | Benson, Susan Semprini, Alex John, Mina Nolan, David Pilgram, Lisa McLean-Tooke, Andrew Plebanski, Magdalena Middleton, Paul Craig, Simon Marais, Ben Waddington, Claire Martin, Andrew Raby, Edward Blyth, Christopher C Tang, Benjamin Lemoh, Chris Denholm, Justin Perez, Gladymar Boyd, Mark Marks, Guy Anstey, Nicholas Richards, Toby McLeod, Charlie Tong, Steve Y C Borland, Meredith L Maze, Michael |
| Contributor_xml | – sequence: 1 givenname: Alex surname: Semprini fullname: Semprini, Alex – sequence: 2 givenname: Andrew surname: Martin fullname: Martin, Andrew – sequence: 3 givenname: Andrew surname: McLean-Tooke fullname: McLean-Tooke, Andrew – sequence: 4 givenname: Ben surname: Marais fullname: Marais, Ben – sequence: 5 givenname: Benjamin surname: Tang fullname: Tang, Benjamin – sequence: 6 givenname: Charlie surname: McLeod fullname: McLeod, Charlie – sequence: 7 givenname: Christopher C surname: Blyth fullname: Blyth, Christopher C – sequence: 8 givenname: Chris surname: Lemoh fullname: Lemoh, Chris – sequence: 9 givenname: Claire surname: Waddington fullname: Waddington, Claire – sequence: 10 givenname: David surname: Nolan fullname: Nolan, David – sequence: 11 givenname: Edward surname: Raby fullname: Raby, Edward – sequence: 12 givenname: Gladymar surname: Perez fullname: Perez, Gladymar – sequence: 13 givenname: Guy surname: Marks fullname: Marks, Guy – sequence: 14 givenname: Justin surname: Denholm fullname: Denholm, Justin – sequence: 15 givenname: Lisa surname: Pilgram fullname: Pilgram, Lisa – sequence: 16 givenname: Nicholas surname: Anstey fullname: Anstey, Nicholas – sequence: 17 givenname: Magdalena surname: Plebanski fullname: Plebanski, Magdalena – sequence: 18 givenname: Mark surname: Boyd fullname: Boyd, Mark – sequence: 19 givenname: Meredith L surname: Borland fullname: Borland, Meredith L – sequence: 20 givenname: Michael surname: Maze fullname: Maze, Michael – sequence: 21 givenname: Mina surname: John fullname: John, Mina – sequence: 22 givenname: Paul surname: Middleton fullname: Middleton, Paul – sequence: 23 givenname: Simon surname: Craig fullname: Craig, Simon – sequence: 24 givenname: Steve Y C surname: Tong fullname: Tong, Steve Y C – sequence: 25 givenname: Susan surname: Benson fullname: Benson, Susan – sequence: 26 givenname: Toby surname: Richards fullname: Richards, Toby |
| Copyright | The Author(s) 2023 2023. The Author(s). COPYRIGHT 2023 BioMed Central Ltd. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2023 – notice: 2023. The Author(s). – notice: COPYRIGHT 2023 BioMed Central Ltd. – notice: 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| CorporateAuthor | COVID BN Advisory Group |
| CorporateAuthor_xml | – name: COVID BN Advisory Group |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU COVID DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1186/s12874-023-01856-1 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One Coronavirus Research Database ProQuest Central Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database Proquest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database MEDLINE |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 6 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1471-2288 |
| EndPage | 21 |
| ExternalDocumentID | oai_doaj_org_article_eb0d3fe90e0e463abcaf0776351625aa 10.1186/s12874-023-01856-1 PMC10050813 A743600704 36991342 10_1186_s12874_023_01856_1 |
| Genre | Research Support, Non-U.S. Gov't Journal Article Observational Study |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: Digital Health CRC – fundername: The Snow Medical Research Foundation – fundername: ; |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACUHS ADBBV ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK IAO IHR INH INR ITC KQ8 M1P M48 MK0 M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC COVID DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM 2VQ 4.4 ADTOC AHSBF C1A EJD H13 HYE IPNFZ RIG UNPAY |
| ID | FETCH-LOGICAL-c608t-ea42669d4e281a9061c5a56031b8fb133c2c3e83316f1b8217085f8b8c1434063 |
| IEDL.DBID | M48 |
| ISSN | 1471-2288 |
| IngestDate | Fri Oct 03 12:52:42 EDT 2025 Sun Oct 26 04:02:19 EDT 2025 Tue Sep 30 17:15:27 EDT 2025 Wed Oct 01 12:58:34 EDT 2025 Tue Oct 07 05:34:13 EDT 2025 Mon Oct 20 22:30:44 EDT 2025 Mon Oct 20 16:18:23 EDT 2025 Mon Jul 21 06:05:11 EDT 2025 Wed Oct 01 05:07:02 EDT 2025 Thu Apr 24 23:01:10 EDT 2025 Sat Sep 06 07:35:31 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | COVID-19 DBN Pathophysiology Experimental design Causal model Expert elicitation DAG Bayesian network Decision support |
| Language | English |
| License | 2023. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c608t-ea42669d4e281a9061c5a56031b8fb133c2c3e83316f1b8217085f8b8c1434063 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s12874-023-01856-1 |
| PMID | 36991342 |
| PQID | 2803011105 |
| PQPubID | 42579 |
| PageCount | 21 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_eb0d3fe90e0e463abcaf0776351625aa unpaywall_primary_10_1186_s12874_023_01856_1 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10050813 proquest_miscellaneous_2792901860 proquest_journals_2803011105 gale_infotracmisc_A743600704 gale_infotracacademiconefile_A743600704 pubmed_primary_36991342 crossref_primary_10_1186_s12874_023_01856_1 crossref_citationtrail_10_1186_s12874_023_01856_1 springer_journals_10_1186_s12874_023_01856_1 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-03-29 |
| PublicationDateYYYYMMDD | 2023-03-29 |
| PublicationDate_xml | – month: 03 year: 2023 text: 2023-03-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC medical research methodology |
| PublicationTitleAbbrev | BMC Med Res Methodol |
| PublicationTitleAlternate | BMC Med Res Methodol |
| PublicationYear | 2023 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | 1856_CR43 Y Wu (1856_CR52) 2021; 149 R Hastie (1856_CR33) 2005; 112 L Chidambaram (1856_CR38) 2005; 16 H Langseth (1856_CR26) 2007; 92 Z Du (1856_CR5) 2021; 6 BW Boehm (1856_CR45) 1988; 5 C Vlek (1856_CR17) 2016; 24 JM Salerno (1856_CR28) 2017; 12 1856_CR10 RT O’Donnell (1856_CR23) 2006 G Charness (1856_CR30) 2012; 26 1856_CR54 N Fenton (1856_CR44) 2000; 37 1856_CR55 1856_CR18 N Shao (1856_CR3) 2020; 43 DJ Packer (1856_CR37) 2009; 20 KB Laskey (1856_CR42) 2000; 12 1856_CR19 S Dhont (1856_CR53) 2020; 21 1856_CR14 1856_CR58 1856_CR16 1856_CR59 BJ Cox (1856_CR46) 1991 JB Soll (1856_CR48) 2004; 30 A Speirs-Bridge (1856_CR49) 2010; 30 YE Chee (1856_CR27) 2016; 82 G Rowe (1856_CR40) 1991; 39 HA Linstone (1856_CR39) 1975 N Bostrom (1856_CR15) 2014 J Pearl (1856_CR12) 1988 1856_CR21 1856_CR65 GJ Fox (1856_CR6) 2020; 212 1856_CR20 1856_CR64 1856_CR67 T Kugler (1856_CR29) 2012; 3 1856_CR66 1856_CR61 1856_CR60 1856_CR63 1856_CR62 C Hitchcock (1856_CR56) 2009; 106 KB Laskey (1856_CR41) 1997 N Fenton (1856_CR11) 2020; 23 1856_CR24 JH Sigurdsson (1856_CR25) 2001; 17 M Stettinger (1856_CR35) 2015 PWG Tennant (1856_CR57) 2020; 50 SG Straus (1856_CR31) 2011; 15 1856_CR2 1856_CR32 1856_CR4 (1856_CR34) 1982 KA Bramstedt (1856_CR1) 2020; 46 A de Ruijter (1856_CR51) 2016; 88 P Spirtes (1856_CR13) 2000 H Wan (1856_CR8) 2020; 9 DG Malcolm (1856_CR47) 1959; 7 MD Mumford (1856_CR36) 2006; 40 JG Richens (1856_CR22) 2020; 11 V Hemming (1856_CR50) 2018; 9 1856_CR7 1856_CR9 |
| References_xml | – volume: 39 start-page: 235 issue: 3 year: 1991 ident: 1856_CR40 publication-title: Technol Forecast Soc Chang. doi: 10.1016/0040-1625(91)90039-I – volume: 7 start-page: 646 year: 1959 ident: 1856_CR47 publication-title: Oper Res. doi: 10.1287/opre.7.5.646 – volume: 23 start-page: 1 year: 2020 ident: 1856_CR11 publication-title: J Risk Res. doi: 10.1080/13669877.2020.1756381 – start-page: 118 volume-title: 23rd International Conference on User Modeling, Adaptation, and Personalization,. UMAP 2015 year: 2015 ident: 1856_CR35 – volume: 16 start-page: 149 issue: 2 year: 2005 ident: 1856_CR38 publication-title: Inf Syst Res. doi: 10.1287/isre.1050.0051 – ident: 1856_CR62 – volume: 43 start-page: 4943 issue: 7 year: 2020 ident: 1856_CR3 publication-title: Math Methods Appl Sci. doi: 10.1002/mma.6345 – volume-title: Causation, Prediction, and Search year: 2000 ident: 1856_CR13 – volume: 26 start-page: 157 issue: 3 year: 2012 ident: 1856_CR30 publication-title: J Econ Perspect. doi: 10.1257/jep.26.3.157 – ident: 1856_CR65 – ident: 1856_CR4 doi: 10.1101/2020.02.20.20023572 – volume: 6 start-page: e184 year: 2021 ident: 1856_CR5 publication-title: Lancet Public Health. doi: 10.1016/S2468-2667(21)00002-5 – ident: 1856_CR14 – volume-title: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference year: 1988 ident: 1856_CR12 – volume: 37 start-page: 61 issue: 1 year: 2000 ident: 1856_CR44 publication-title: Math Today. – ident: 1856_CR18 doi: 10.1145/3322640.3326716 – volume: 30 start-page: 512 issue: 3 year: 2010 ident: 1856_CR49 publication-title: Risk Anal. doi: 10.1111/j.1539-6924.2009.01337.x – ident: 1856_CR63 – ident: 1856_CR64 – ident: 1856_CR43 – ident: 1856_CR32 doi: 10.1002/9780470561119.socpsy002032 – volume: 9 start-page: 169 issue: 1 year: 2018 ident: 1856_CR50 publication-title: Methods Ecol Evol. doi: 10.1111/2041-210X.12857 – volume: 88 start-page: 211 year: 2016 ident: 1856_CR51 publication-title: Saf Sci. doi: 10.1016/j.ssci.2016.03.001 – ident: 1856_CR19 – volume-title: Object-Oriented Programming; An Evolutionary Approach year: 1991 ident: 1856_CR46 – volume: 21 start-page: 1 issue: 1 year: 2020 ident: 1856_CR53 publication-title: Respir Res. doi: 10.1186/s12931-020-01462-5 – volume: 149 year: 2021 ident: 1856_CR52 publication-title: Epidemiol Infect. doi: 10.1017/S0950268821001357 – start-page: 316 volume-title: The Cambridge Handbook of Artificial Intelligence year: 2014 ident: 1856_CR15 doi: 10.1017/CBO9781139046855.020 – volume: 15 start-page: 128 issue: 2 year: 2011 ident: 1856_CR31 publication-title: Group Dyn: Theory Res Pract. doi: 10.1037/a0022734 – volume: 112 start-page: 494 year: 2005 ident: 1856_CR33 publication-title: Psychol Rev. doi: 10.1037/0033-295X.112.2.494 – ident: 1856_CR54 – ident: 1856_CR10 doi: 10.1101/2020.04.10.20060426 – volume: 17 start-page: 181 issue: 3 year: 2001 ident: 1856_CR25 publication-title: Qual Reliab Eng Int. doi: 10.1002/qre.410 – volume: 30 start-page: 299 issue: 2 year: 2004 ident: 1856_CR48 publication-title: J Exp Psychol Learn Mem Cogn. doi: 10.1037/0278-7393.30.2.299 – volume: 92 start-page: 92 issue: 1 year: 2007 ident: 1856_CR26 publication-title: Reliab Eng Syst Saf. doi: 10.1016/j.ress.2005.11.037 – ident: 1856_CR60 – ident: 1856_CR67 – ident: 1856_CR16 – volume: 50 start-page: 620 issue: 2 year: 2020 ident: 1856_CR57 publication-title: Int J Epidemiol. doi: 10.1093/ije/dyaa213 – volume: 9 start-page: 116 year: 2020 ident: 1856_CR8 publication-title: Infect Dis Poverty. doi: 10.1186/s40249-020-00683-6 – volume: 24 start-page: 285 year: 2016 ident: 1856_CR17 publication-title: Artif Intell Law. doi: 10.1007/s10506-016-9183-4 – volume: 12 issue: 9 year: 2017 ident: 1856_CR28 publication-title: PLoS ONE. doi: 10.1371/journal.pone.0183580 – volume: 12 start-page: 487 issue: 4 year: 2000 ident: 1856_CR42 publication-title: IEEE Trans Knowl Data Eng. doi: 10.1109/69.868902 – ident: 1856_CR58 – volume: 20 start-page: 546 issue: 5 year: 2009 ident: 1856_CR37 publication-title: Psychol Sci. doi: 10.1111/j.1467-9280.2009.02333.x – volume: 3 start-page: 471 issue: 4 year: 2012 ident: 1856_CR29 publication-title: Wiley Interdiscip Rev Cogn Sci. doi: 10.1002/wcs.1184 – volume-title: Judgment Under Uncertainty: Heuristics and Biases year: 1982 ident: 1856_CR34 – volume: 106 start-page: 587 issue: 11 year: 2009 ident: 1856_CR56 publication-title: J Philos. doi: 10.5840/jphil20091061128 – ident: 1856_CR21 doi: 10.24963/ijcai.2017/202 – volume: 5 start-page: 61 year: 1988 ident: 1856_CR45 publication-title: Computer. doi: 10.1109/2.59 – start-page: 192 volume-title: AI 2006: Advances in Artificial Intelligence year: 2006 ident: 1856_CR23 doi: 10.1007/11941439_23 – volume-title: The Delphi Method: Techniques and Applications year: 1975 ident: 1856_CR39 – volume: 40 start-page: 75 issue: 2 year: 2006 ident: 1856_CR36 publication-title: J Creat Behav. doi: 10.1002/j.2162-6057.2006.tb01267.x – start-page: 334 volume-title: Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, UAI’97 year: 1997 ident: 1856_CR41 – ident: 1856_CR2 – ident: 1856_CR24 – ident: 1856_CR61 – ident: 1856_CR66 – ident: 1856_CR20 doi: 10.1111/risa.13759 – volume: 212 start-page: 468 issue: 10 year: 2020 ident: 1856_CR6 publication-title: Med J Aust. doi: 10.5694/mja2.50606 – ident: 1856_CR9 doi: 10.12688/wellcomeopenres.15881.1 – volume: 82 start-page: 108 year: 2016 ident: 1856_CR27 publication-title: Environ Model Softw. doi: 10.1016/j.envsoft.2016.04.012 – volume: 46 start-page: 803 issue: 12 year: 2020 ident: 1856_CR1 publication-title: J Med Ethics. doi: 10.1136/medethics-2020-106494 – volume: 11 start-page: 3923 year: 2020 ident: 1856_CR22 publication-title: Nat Commun. doi: 10.1038/s41467-020-17419-7 – ident: 1856_CR55 – ident: 1856_CR7 doi: 10.1016/S1473-3099(20)30144-4 – ident: 1856_CR59 |
| SSID | ssj0017836 |
| Score | 2.437173 |
| Snippet | Background
COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms... COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved,... Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms... BackgroundCOVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are... Abstract Background COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological... |
| SourceID | doaj unpaywall pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 76 |
| SubjectTerms | Algorithms Bayes Theorem Bayesian network Bayesian statistical decision theory Causal model COVID-19 COVID-19 - epidemiology DAG Databases, Factual Datasets DBN Disease transmission Evaluation Feedback Health Sciences Humans Knowledge Medical research Medicine Medicine & Public Health Models, Theoretical Pathophysiology Random variables SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 Software Statistical Theory and Methods Statistics for Life Sciences Subject specialists Theory of Medicine/Bioethics Time series |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6hHngcKt6kFGQkJA40ahwnXvvYFqqCBFwo6s2yHUcFLdlVsyu0_56ZOAkbkAoHrrEdxZ5x5hvNzDcAL610sp6VeaptXqUF93nqpPWpqniospnPXczy_SjPzov3F-XFVqsvygmL9MDx4A6DyypRB52FLBRSWOdtTRQ0ouQI3W0HjTKlB2eqjx9QbcJQIqPkYcuJ1j1F-4SusyLmvYkZ6tj6__wnbxml3xMmx6jpHbi1bpZ288PO51uG6fQu7PaIkh3FndyDG6G5Dzc_9DHzB_CN2p1R0Tk7-fTl3ZuUa9ZHZdgyVgmElrkNuwootcBwqu95u9miZt6uW3z7sd0EKrdkTUwbbxnVpbDvMczDuj4Bq_YhnJ--_XxylvYdFlIvM7VKgyUDrasi5Ipbjbbdl7akxtNO1Q7dV597EZQQXNb4CN0XRGi1csojzEIoIB7BTrNowhNguLwSFr0n56tCZg5hj51JbTkirjIUdQJ8OHAzbIO6YMxN54YoaaKQDArJdEIyPIHX45plJN-4dvYxyXGcScTZ3QNUJ9Ork_mbOiXwirTA0PXGz_O2r1LATRJRljlCxEWU_lmRwP5kJl5LPx0e9Mj0v4XWUCsw_KEipk3gxThMKynVrQmLNc6ZaYptK5kl8Diq3bglITVlSuQJqIlCTvY8HWm-Xnak4ZyYfhQXCRwMuvvru6471INRv_9BBnv_QwZP4XbeXVXqMrgPO6urdXiG0G_lnne3_Ccvq1Dk priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED-NTuLjAfFNYCAjIfHAosX5cJ0HhNaxaSBREGJob5btOHyopKVphfrfc5c42QJSxWtsV3Hu7Lvr3f1-AM-1MKIcZ3GY67gIU27j0AhtQ1lwV0RjG5u2yncqTs_Sd-fZ-Q5Mu14YKqvs7sTmoi7mlv4jPyAWJeJFj7LXi18hsUZRdrWj0NCeWqF41UCMXYHdmJCxRrA7OZ5-_NTnFahnoWudkeKg5gT3HqLdwpBaEiLfwDw1KP7_3tWXjNXfhZR9NvUGXFtXC735rWezSwbr5Bbc9J4mO2xV4zbsuOoOXH3vc-l34QfRoFEzOjv68OXtm5DnzGdr2KLtHnA1Mxu2dChNx3Cq9XjebF4yq9c1_vpEbxy1YbKqLSevGfWrsJ9t-oc1_AGr-h6cnRx_PjoNPfNCaEUkV6HTZLjzInWx5DpHm28znREhtZGlwbDWxjZxMkm4KPERhjXouZXSSIvuF7oIyX0YVfPKPQSGy4tEY1RlbJGKyKA7pMci1xw9scylZQC8--Cq2waxY8xUE55IoVohKRSSaoSkeAAv-zWLFpRj6-wJybGfSYDazYP58qvy51M5ExVJ6fLIRS4ViTZWl4R0lGQcI0StA3hBWqDo2OPrWe27F3CTBKClDtETI6j_KA1gbzATj6sdDnd6pPx1UasL5Q7gWT9MK6kErnLzNc4Z55TzliIK4EGrdv2WEpFTBUUcgBwo5GDPw5Hq-7cGTJwTApDkSQD7ne5evNe2j7rf6_d_yODR9l0_hutxcwiJV3APRqvl2j1BZ29lnvoT_Ackf04D priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lj9QwDLZgkXgcEG8KCwoSEgc2omnaTHrcHVgtSMCFRXuLkjQVoKEz2s4Izb_HTh9MAa3g2jhVU9uxLdufAZ5b5VQ9KzJe2qziufAZd8p6risRqnTmM9dV-X5QJ6f5u7PirIfJoV6Y3fy90OpVKwiQnaNlwaBXE2beZbiCRkrFxKyajxkD6kYYmmL-um9ieCI-_5-38I4Z-r1EcsyT3oBrm2Zltz_sYrFjio5vwc3eh2SHHdNvw6XQ3IGr7_ss-V34RgPOqM2czT9-fvuai5L1eRi26voCQsvclp0H5FNgSOp7pG62rJm3mxbffmS3gRosWdMVireMOlHY9y6xw-JkgHV7D06P33yan_B-pgL3KtVrHiyZ5LLKQ6aFLdGa-8IWNGra6dphwOozL4OWUqgaH2HAgj5ZrZ326Fih8Zf3Ya9ZNuEhMNxeSYvxkvNVrlKHjo6dqdIic_Ii5HUCYvjhZjgGzb1YmBh4aGU6JhlkkolMMiKBl-OeVQe3cSH1EfFxpCSo7PgAJcj0mmeCSytZhzINaciVtM7bmjCMZCEw9rM2gRckBYYUGj_P274vAQ9J0FjmEH0sAvFP8wT2J5SoiH66PMiR6S-C1tDwL7xC0YtN4Nm4TDupuK0Jyw3SzErKZmuVJvCgE7vxSFKVVBuRJaAnAjk583Sl-folwoQLwvbRQiZwMMjur--66KcejPL9Dzx49H9vfwzXs6iUNEFwH_bW55vwBN26tXsa9fknNQZAgw priority: 102 providerName: Springer Nature – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Zb9QwEB6VrcTxwH0ECjISEg802ziH13ncFqqCREGIReXJsh2Ha5tdbXaFll_PTC42BVUg8RqPo9gZj7_RzHwD8EQLI_JREvqpDjM_5jb0jdDWlxl3WTCyoamzfI_F0SR-dZKcbMG7thbGnFq8BdDTrBsoz6bDzTL0aV3lQF0U3GJvnuX1oZdir-RE3O7jDYTOsSRuvQuwLRLE5wPYnhy_HX-syoxG3A9DKdvqmT9O7N1QFZH_7-Z64746m0vZBVSvwKVVMdfr73o63bizDq9B2a62TlX5NlwtzdD-OEME-X-34zpcbSAuG9c6eQO2XHETLr5ugvi34Cv1X6MqeHbw5sPL5z5PWRMmYvO6bMGVzKzZwqEaOYaitiESZ7OcWb0q8e37eu2o_pMVdR57yahQhp3WcSdWNS5Ylrdhcvji_cGR37R88K0I5NJ3mhBDmsUulFynCDZsohPqhG1kbtCftqGNnIwiLnJ8hP4UQsZcGmkR9yE2ie7AoJgV7h4wnJ5FGt05Y7NYBAZxmB6JVHOEgImLcw94-5tVuwxqyzFVlV8khao3UeEmqmoTFffgWTdnXrOBnCu9T9rTSRKTd_VgtvikGsOgnAmyKHdp4AIXi0gbq3OiWIoSjq6p1h48Jd1TZG_w86xuyiZwkcTcpcYIAanHQBB7sNOTRDth-8Ot9qrGTpWKepOhhUeQ7cHjbphmUu5d4WYrlBmlFGyXIvDgbq3s3ZIikVLqRuiB7B2D3pr7I8WXzxWLOSfqIckjD3bbE_Pru87b1N3uVP3FP7j_b-IP4HJYHRpqcLgDg-Vi5R4i6lyaR40Z-Qla7nkQ priority: 102 providerName: Unpaywall |
| Title | Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts |
| URI | https://link.springer.com/article/10.1186/s12874-023-01856-1 https://www.ncbi.nlm.nih.gov/pubmed/36991342 https://www.proquest.com/docview/2803011105 https://www.proquest.com/docview/2792901860 https://pubmed.ncbi.nlm.nih.gov/PMC10050813 https://bmcmedresmethodol.biomedcentral.com/counter/pdf/10.1186/s12874-023-01856-1 https://doaj.org/article/eb0d3fe90e0e463abcaf0776351625aa |
| UnpaywallVersion | publishedVersion |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: RBZ dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: KQ8 dateStart: 20010101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: ABDBF dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: DIK dateStart: 20010101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: GX1 dateStart: 0 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: RPM dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals Open Access customDbUrl: eissn: 1471-2288 dateEnd: 20250131 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: M48 dateStart: 20011101 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal – providerCode: PRVAVX databaseName: HAS SpringerNature Open Access 2022 customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: AAJSJ dateStart: 20011201 isFulltext: true titleUrlDefault: https://www.springernature.com providerName: Springer Nature – providerCode: PRVAVX databaseName: Springer Nature OA Free Journals customDbUrl: eissn: 1471-2288 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0017836 issn: 1471-2288 databaseCode: C6C dateStart: 20010112 isFulltext: true titleUrlDefault: http://www.springeropen.com/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Zb9NAEB71kDgeEDeGEi0SEg_U4PWxXj8glIRWBamhqghqeVmt12sOBSfEiSD_nhkfaQ1VBS-R4p21vDsznhnPzjcAT7VIRR5HvptoP3NDbnw3Fdq4MuM282Ljp_Up35E4GIfvTqKTDWjbHTUbWF4Y2lE_qfF88uLXj9VrVPhXlcJL8bLkBNruovXBwFgSrt4mbKOlSqiVw2F4llWgioWq2ijmru9L2RbRXHiPjqGq8Pz_fmufM1t_Hqlc51Wvw9VlMdOrn3oyOWe69m_CjcbnZP1aSG7Bhi1uw5XDJqt-B75RQzQqS2fD9x_fvnF5wpq8DZvVdQS2ZOmKzS3y1TIkNQ2yN5vmzOhliXcf6JWlgkxW1AfLS0aVK-x7nQhiVSeBRXkXxvt7H4YHbtODwTXCkwvXajLhSRZaX3KdoPU3kY6oNXUq8xQDXOObwMog4CLHSxjgoA-Xy1QadMTQWQjuwVYxLewDYDg9CzTGV6nJQuGl6BjpWCSao08W2TB3gLcbrtplUJ-MiaoCFSlUzSSFTFIVkxR34Pl6zqyG57iUekB8XFMStHZ1YTr_rBpNVTb1siC3iWc9G4pAp0bnhHkURBxjRa0deEZSoEgk8fGMbuoYcJEEpaX66JMR6L8XOrDToUTFNd3hVo5UK_eKmoXhKxe9XgeerIdpJh2GK-x0iTRxQtlvKTwH7tdit15SIBI6S-E7IDsC2Vlzd6T4-qWCFeeEBSR54MBuK7tnz3XZpu6u5fsfePDwvzj2CK75lU5Sw8Ed2FrMl_YxeoGLtAeb8Uncg-3B3ujoGP8NxbBXfVHpVUqPv8eDTzg-Hh31T38DinhXjw |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Zb9NAEB6VVqLwgLgxFFgkEA_UqtfHZv1QoaaHEtoGhNqqb8t6veZQcEKcqMqf47cx46sNSBEvfbO8u5bXMzuH5_gAXmuRiKwT-W6s_dQNufHdRGjjypTb1OsYP6myfAeidxp-OI_OV-B3UwtDaZWNTCwFdToy9I98i1CUCBfdi96Pf7mEGkXR1QZCQ9fQCul22WKsLuw4tPMLdOGK7f4e0vuN7x_sn-z23BplwDXCk1PXalJScRpaX3Ido34zkY4IfDmRWYIunPFNYGUQcJHhLTTh0UrJZCINmhqoDgN87g1Yw-sYnb-17v7g0-c2jkE1Ek2pjhRbBaf28i7qSXThJXUAXFCHJWrAv7rhinL8O3Gzjd7ehvVZPtbzCz0cXlGQB3fhTm3Zsp2KFe_Bis3vw83jOnb_AH4Q7BoVv7Pdj2f9PZfHrI4OsXFVrWALlszZxCL3WIZTTd0_nI0yZvSswKd39dxS2SfLq_T1glF9DPtZhZtYiVcwLR7C6bXQ4BGs5qPcPgGGy9NAoxeXmDQUXoLml-6IWHO0_CIbZg7w5oOrZhuExjFUpTskhaqIpJBIqiSS4g68a9eMqyYgS2d3iY7tTGrgXd4YTb6qWh4om3hpkNnYs54NRaATozPqrBREHD1SrR14S1ygSMzg6xldV0vgJqlhl9pBy4-gBbzQgY2FmSgezOJww0eqFk-FujxMDrxqh2klpdzldjTDOZ2YYuxSeA48rtiu3VIgYsrY8B2QCwy5sOfFkfz7t7J5OaeOQ5IHDmw2vHv5Xss-6mbL3_9Bg6fLd_0S1nsnx0fqqD84fAa3_PJAEqbhBqxOJzP7HA3NafKiPs0Mvly3APkDa8WIZQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9QwDLdgSGM8IL4pDAgSEg-sWtOPXPq43ThtfAweGNpblKQpDB290_VO6P577KYtV0ATvDZO1dR2bMv2zwAvtDCiHGVxmOu4CFNu49AIbUNZcFdEIxsbX-V7Ko7P0jfn2flGF39T7d6lJH1PA6E0Vcv9eVF6FZdiv-YE0x6ivcFQWBKS3lW4lqJ1oxkGYzHu8wjUo9C1yvx138AcNaj9f97NG8bp98LJPnt6A66vqrle_9DT6YaBmtyCm61nyQ68KNyGK666A9vv29z5XfhGY8-o-ZyNP3w-OQp5ztrsDJv7bgFXM7NmC4fccwxJbYvfzWYls3pV49sP9dpR2yWrfPl4zag_hX336R7WzAtY1vfgbPL60_g4bCcthFZEchk6TYY6L1IXS65ztPE20xkNoDayNBjG2tgmTiYJFyU-wjAGPbVSGmnR3UKXILkPW9Wscg-B4fYi0RhFGVukIjLo_uiRyDVHzytzaRkA73646o5B0zCmqglHpFCeSQqZpBomKR7Aq37P3INwXEp9SHzsKQlAu3kwW3xRrT4qZ6IiKV0eucilItHG6pKQjZKMY0SodQAvSQoUqTl-ntVttwIekgCz1AF6XgTtH6UB7A4oUT3tcLmTI9VeD7WikWB4saJvG8Dzfpl2Uslb5WYrpBnllOOWIgrggRe7_kiJyKliIg5ADgRycObhSnXxtQEP54T4I3kSwF4nu7--67KfutfL9z_w4NH_vf0ZbH88mqh3J6dvH8NO3OgnjRjcha3lYuWeoN-3NE8b1f4J1o1LuQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Zb9QwEB6VrcTxwH0ECjISEg802ziH13ncFqqCREGIReXJsh2Ha5tdbXaFll_PTC42BVUg8RqPo9gZj7_RzHwD8EQLI_JREvqpDjM_5jb0jdDWlxl3WTCyoamzfI_F0SR-dZKcbMG7thbGnFq8BdDTrBsoz6bDzTL0aV3lQF0U3GJvnuX1oZdir-RE3O7jDYTOsSRuvQuwLRLE5wPYnhy_HX-syoxG3A9DKdvqmT9O7N1QFZH_7-Z64746m0vZBVSvwKVVMdfr73o63bizDq9B2a62TlX5NlwtzdD-OEME-X-34zpcbSAuG9c6eQO2XHETLr5ugvi34Cv1X6MqeHbw5sPL5z5PWRMmYvO6bMGVzKzZwqEaOYaitiESZ7OcWb0q8e37eu2o_pMVdR57yahQhp3WcSdWNS5Ylrdhcvji_cGR37R88K0I5NJ3mhBDmsUulFynCDZsohPqhG1kbtCftqGNnIwiLnJ8hP4UQsZcGmkR9yE2ie7AoJgV7h4wnJ5FGt05Y7NYBAZxmB6JVHOEgImLcw94-5tVuwxqyzFVlV8khao3UeEmqmoTFffgWTdnXrOBnCu9T9rTSRKTd_VgtvikGsOgnAmyKHdp4AIXi0gbq3OiWIoSjq6p1h48Jd1TZG_w86xuyiZwkcTcpcYIAanHQBB7sNOTRDth-8Ot9qrGTpWKepOhhUeQ7cHjbphmUu5d4WYrlBmlFGyXIvDgbq3s3ZIikVLqRuiB7B2D3pr7I8WXzxWLOSfqIckjD3bbE_Pru87b1N3uVP3FP7j_b-IP4HJYHRpqcLgDg-Vi5R4i6lyaR40Z-Qla7nkQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Modeling+COVID-19+disease+processes+by+remote+elicitation+of+causal+Bayesian+networks+from+medical+experts&rft.jtitle=BMC+medical+research+methodology&rft.au=Mascaro%2C+Steven&rft.au=Wu%2C+Yue&rft.au=Woodberry%2C+Owen&rft.au=Nyberg%2C+Erik+P.&rft.date=2023-03-29&rft.issn=1471-2288&rft.eissn=1471-2288&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1186%2Fs12874-023-01856-1&rft.externalDBID=n%2Fa&rft.externalDocID=10_1186_s12874_023_01856_1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2288&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2288&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2288&client=summon |