ArticleAmerican journal of epidemiology2015
Invited commentary: The virtual epidemiologist—promise and peril.
Article in American journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Applications of agent-based modeling in trauma research.Psychological trauma : theory, research, practice and policy · 2023Article
- Data-rich modeling helps answer increasingly complex questions on variant and disease interactions: Comment on "Mathematical models for dengue fever epidemiology: A 10-year systematic review" by Aguiar et al.Physics of life reviews · 2023Article
- Dynamical Modeling as a Tool for Inferring Causation.American journal of epidemiology · 2022Article
- G-Computation and Agent-Based Modeling for Social Epidemiology: Can Population Interventions Prevent Posttraumatic Stress Disorder?American journal of epidemiology · 2022Article
- Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention.PLoS computational biology · 2021Article
- Advancing the study of health inequality: Fundamental causes as systems of exposure.SSM - population health · 2020Article
- Systems Modeling to Advance the Promise of Data Science in Epidemiology.American journal of epidemiology · 2019Article
- DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference.International journal of epidemiology · 2019Article
- Agent-Based Modeling in Public Health: Current Applications and Future Directions.Annual review of public health · 2018Review
- Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.American journal of epidemiology · 2017Article
- Transdisciplinary approaches enhance the production of translational knowledge.Translational research : the journal of laboratory and clinical medicine · 2017Review
- Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models.BMC public health · 2015Article
- Marshall and Galea respond to "data theory in epidemiology".American journal of epidemiology · 2015Article
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The simulation of complex systems has received increasing attention as a useful approach in epidemiology. As discussed by Marshall and Galea in this issue of the Journal (Am J Epidemiol. 2015;181(2):92-99), systems approaches are appealing because they allow explicit recognition of feedback, interference, adaptation over time, and nonlinearities. However, they differ fundamentally from the traditional approaches to causal inference used in epidemiology in that they involve creation of a virtual world. Systems modeling can help us understand the plausible implications of the knowledge that we have and how pieces can act together in ways that we might not have predicted. It can help us integrate quantitative and qualitative information and explore basic dynamics. It can generate new questions that can be investigated through new observations or experiments. The process of building a systems model forces us to think about dynamic relationships and the ways in which they may play a role in the process we are studying. However, the validity of any causal conclusions derived from systems models hinges on the extent to which the models represent the fundamental dynamics relevant to the process in the real world. For this reason, systems modeling will never replace causal inference based on empirical observation. Causal inference based on empirical observation and simulation modeling serve interrelated but different purposes.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.