Abstract
Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 862-865 |
| Number of pages | 4 |
| Journal | American journal of epidemiology |
| Volume | 188 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 1 2019 |
| Externally published | Yes |
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Keywords
- agent-based models
- complex systems
- public health
- systems science
ASJC Scopus subject areas
- Epidemiology
Cite this
Systems Modeling to Advance the Promise of Data Science in Epidemiology. / Cerda, Magdalena; Keyes, Katherine M.
In: American journal of epidemiology, Vol. 188, No. 5, 01.05.2019, p. 862-865.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Systems Modeling to Advance the Promise of Data Science in Epidemiology
AU - Cerda, Magdalena
AU - Keyes, Katherine M.
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.
AB - Systems science models use computer-based algorithms to model dynamic interactions between study units within and across levels and are characterized by nonlinear and feedback processes. They are particularly valuable approaches that complement the traditional epidemiologic toolbox in cases in which real data are not available and in cases in which traditional epidemiologic methods are limited by issues such as interference, spatial dependence, and dynamic feedback processes. In this commentary, we propose 2 key contributions that systems models can make to epidemiology: 1) the ability to test assumptions about underlying mechanisms that give rise to population distributions of disease; and 2) help in identifying the types of interventions that have the greatest potential to reduce population rates of disease in the future or in new sites where they have not yet been implemented. We discuss central challenges in the application of systems science approaches in epidemiology, propose potential solutions, and predict future developments in the role that systems science can play in epidemiology.
KW - agent-based models
KW - complex systems
KW - public health
KW - systems science
UR - http://www.scopus.com/inward/record.url?scp=85065553848&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065553848&partnerID=8YFLogxK
U2 - 10.1093/aje/kwy262
DO - 10.1093/aje/kwy262
M3 - Article
C2 - 30877289
AN - SCOPUS:85065553848
VL - 188
SP - 862
EP - 865
JO - American Journal of Epidemiology
JF - American Journal of Epidemiology
SN - 0002-9262
IS - 5
ER -