Social network analysis and agent-based modeling in social epidemiology.

The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literat...

Celý popis

Podrobná bibliografie
Hlavní autoři: El-Sayed, A, Scarborough, P, Seemann, L, Galea, S
Médium: Journal article
Jazyk:English
Vydáno: 2012
_version_ 1826270611956039680
author El-Sayed, A
Scarborough, P
Seemann, L
Galea, S
author_facet El-Sayed, A
Scarborough, P
Seemann, L
Galea, S
author_sort El-Sayed, A
collection OXFORD
description The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed.
first_indexed 2024-03-06T21:43:34Z
format Journal article
id oxford-uuid:48c7378d-7c6b-497d-a4db-c99239925dec
institution University of Oxford
language English
last_indexed 2024-03-06T21:43:34Z
publishDate 2012
record_format dspace
spelling oxford-uuid:48c7378d-7c6b-497d-a4db-c99239925dec2022-03-26T15:27:40ZSocial network analysis and agent-based modeling in social epidemiology.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:48c7378d-7c6b-497d-a4db-c99239925decEnglishSymplectic Elements at Oxford2012El-Sayed, AScarborough, PSeemann, LGalea, S The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed.
spellingShingle El-Sayed, A
Scarborough, P
Seemann, L
Galea, S
Social network analysis and agent-based modeling in social epidemiology.
title Social network analysis and agent-based modeling in social epidemiology.
title_full Social network analysis and agent-based modeling in social epidemiology.
title_fullStr Social network analysis and agent-based modeling in social epidemiology.
title_full_unstemmed Social network analysis and agent-based modeling in social epidemiology.
title_short Social network analysis and agent-based modeling in social epidemiology.
title_sort social network analysis and agent based modeling in social epidemiology
work_keys_str_mv AT elsayeda socialnetworkanalysisandagentbasedmodelinginsocialepidemiology
AT scarboroughp socialnetworkanalysisandagentbasedmodelinginsocialepidemiology
AT seemannl socialnetworkanalysisandagentbasedmodelinginsocialepidemiology
AT galeas socialnetworkanalysisandagentbasedmodelinginsocialepidemiology