Situating agent-based modelling in population health research.

IF 3.6 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Emerging Themes in Epidemiology Pub Date : 2021-07-30 DOI:10.1186/s12982-021-00102-7
Eric Silverman, Umberto Gostoli, Stefano Picascia, Jonatan Almagor, Mark McCann, Richard Shaw, Claudio Angione
{"title":"Situating agent-based modelling in population health research.","authors":"Eric Silverman, Umberto Gostoli, Stefano Picascia, Jonatan Almagor, Mark McCann, Richard Shaw, Claudio Angione","doi":"10.1186/s12982-021-00102-7","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":39896,"journal":{"name":"Emerging Themes in Epidemiology","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325181/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Emerging Themes in Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12982-021-00102-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于主体的模型在人口健康研究中的定位。
当今最棘手的人口健康挑战往往是由社会和环境决定因素驱动的,而使用传统的流行病学方法很难对其进行建模。我们同意那些主张在应对这些挑战时更广泛地采用基于代理的建模(ABM)的人的观点。然而,尽管反导偶尔在人口健康中使用,但我们认为,反导要想在该领域最有效,就应该作为回答传统流行病学工具包通常无法回答的问题的手段。为了清楚地说明ABM在人口健康研究中的效用,并澄清对该方法概念基础的持续误解,我们详细介绍了复杂系统理论的核心概念,并总结了为什么模拟对复杂系统的研究至关重要。然后,我们考察了人口健康ABM的现状,并提出它们非常适合研究人口健康中的“邪恶”问题,并可能对这些领域的理论和干预发展做出重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Emerging Themes in Epidemiology
Emerging Themes in Epidemiology Medicine-Epidemiology
CiteScore
4.40
自引率
4.30%
发文量
9
审稿时长
28 weeks
期刊介绍: Emerging Themes in Epidemiology is an open access, peer-reviewed, online journal that aims to promote debate and discussion on practical and theoretical aspects of epidemiology. Combining statistical approaches with an understanding of the biology of disease, epidemiologists seek to elucidate the social, environmental and host factors related to adverse health outcomes. Although research findings from epidemiologic studies abound in traditional public health journals, little publication space is devoted to discussion of the practical and theoretical concepts that underpin them. Because of its immediate impact on public health, an openly accessible forum is needed in the field of epidemiology to foster such discussion.
期刊最新文献
Explaining biological differences between men and women by gendered mechanisms. Population cause of death estimation using verbal autopsy methods in large-scale field trials of maternal and child health: lessons learned from a 20-year research collaboration in Central Ghana. Dynamics of COVID-19 progression and the long-term influences of measures on pandemic outcomes. Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research. Geographical clustering and geographically weighted regression analysis of home delivery and its determinants in developing regions of Ethiopia: a spatial analysis.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1