ArticlePrevention science : the official journal of the Society for Prevention Research2022
High-Fidelity Agent-Based Modeling to Support Prevention Decision-Making: an Open Science Approach.
Article in Prevention science : the official journal of the Society for Prevention Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Analyzing audience mental health through the communication and structure of complex networked online music culture.Scientific reports · 2026Article
- What scientific inferences can be made with randomized implementation rollout trials.Implementation science : IS · 2025Article
- The Practical, Robust Implementation and Sustainability (PRISM)-capabilities model for use of Artificial Intelligence in community-engaged implementation science research.Implementation science : IS · 2025Article
- Model-driven decision support: A community-based meta-implementation strategy to predict population impact.Annals of epidemiology · 2024Article
- Overdose deaths before and during the COVID-19 pandemic in a US county.Frontiers in public health · 2024Article
- Modelling HIV/AIDS epidemiological complexity: A scoping review of Agent-Based Models and their application.PloS one · 2024Article
- A structured approach to applying systems analysis methods for examining implementation mechanisms.Implementation science communications · 2023Article
- Guidance on the use of complex systems models for economic evaluations of public health interventions.Health economics · 2023Review
- Article
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Authors and funding
4 authors.
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Abstract
Preventing adverse health outcomes is complex due to the multi-level contexts and social systems in which these phenomena occur. To capture both the systemic effects, local determinants, and individual-level risks and protective factors simultaneously, the prevention field has called for adoption of system science methods in general and agent-based models (ABMs) specifically. While these models can provide unique and timely insight into the potential of prevention strategies, an ABM's ability to do so depends strongly on its accuracy in capturing the phenomenon. Furthermore, for ABMs to be useful, they need to be accepted by and available to decision-makers and other stakeholders. These two attributes of accuracy and acceptability are key components of open science. To ensure the creation of high-fidelity models and reliability in their outcomes and consequent model-based decision-making, we present a set of recommendations for adopting and using this novel method. We recommend ways to include stakeholders throughout the modeling process, as well as ways to conduct model verification, validation, and replication. Examples from HIV and overdose prevention work illustrate how these recommendations can be applied.
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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.