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Situating agent-based modelling in population health research
Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325181/ https://www.ncbi.nlm.nih.gov/pubmed/34330302 http://dx.doi.org/10.1186/s12982-021-00102-7 |
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author | Silverman, Eric Gostoli, Umberto Picascia, Stefano Almagor, Jonatan McCann, Mark Shaw, Richard Angione, Claudio |
author_facet | Silverman, Eric Gostoli, Umberto Picascia, Stefano Almagor, Jonatan McCann, Mark Shaw, Richard Angione, Claudio |
author_sort | Silverman, Eric |
collection | PubMed |
description | Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas. |
format | Online Article Text |
id | pubmed-8325181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83251812021-08-02 Situating agent-based modelling in population health research Silverman, Eric Gostoli, Umberto Picascia, Stefano Almagor, Jonatan McCann, Mark Shaw, Richard Angione, Claudio Emerg Themes Epidemiol Analytic Perspective Today’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas. BioMed Central 2021-07-30 /pmc/articles/PMC8325181/ /pubmed/34330302 http://dx.doi.org/10.1186/s12982-021-00102-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Analytic Perspective Silverman, Eric Gostoli, Umberto Picascia, Stefano Almagor, Jonatan McCann, Mark Shaw, Richard Angione, Claudio Situating agent-based modelling in population health research |
title | Situating agent-based modelling in population health research |
title_full | Situating agent-based modelling in population health research |
title_fullStr | Situating agent-based modelling in population health research |
title_full_unstemmed | Situating agent-based modelling in population health research |
title_short | Situating agent-based modelling in population health research |
title_sort | situating agent-based modelling in population health research |
topic | Analytic Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325181/ https://www.ncbi.nlm.nih.gov/pubmed/34330302 http://dx.doi.org/10.1186/s12982-021-00102-7 |
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