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Philippe J. Giabbanelli
Researcher at Miami University
Publications - 30
Citations - 283
Philippe J. Giabbanelli is an academic researcher from Miami University. The author has contributed to research in topics: Computer science & Population. The author has an hindex of 6, co-authored 30 publications receiving 106 citations.
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Journal ArticleDOI
Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study.
TL;DR: In this paper, the effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans was investigated.
Journal ArticleDOI
What's left before participatory modeling can fully support real-world environmental planning processes: A case study review
Beatrice Hedelin,Steven Gray,S. Woehlke,Todd K. BenDor,Alison Singer,Rebecca Jordan,Moira Zellner,Philippe J. Giabbanelli,Pierre D. Glynn,Karen E. Jenni,Antonie J. Jetter,Nagesh Kolagani,Bethany K. Laursen,Kirsten M. Leong,L. Schmitt Olabisi,Eleanor J. Sterling +15 more
TL;DR: It is found that significant work likely remains for PM to fully support participatory and integrated planning processes and key research and practice issues for improving PM as an approach for real-world participatory planning and governance are reviewed.
Proceedings ArticleDOI
The intersection of agent based models and fuzzy cognitive maps: a review of an emerging hybrid modeling practice
TL;DR: This review provides a snapshot of an emerging field, thus assembling the evidence-base to identify potential areas for future work, such as consolidating and standardizing software development efforts in a currently fragmented field.
Journal ArticleDOI
The application of modeling and simulation to public health: Assessing the quality of Agent-Based Models for obesity
TL;DR: In this article, the authors provide a systematic review of agent-based models of obesity for public health and assess the extent to which these models can be trusted to support public health decisions.
Proceedings ArticleDOI
Solving challenges at the interface of simulation and big data using machine learning
TL;DR: This analysis is devoted on three relatively under-studied stages: calibrating a simulation model using ML, dealing with the issues of large search space by employing ML for experimentation, and identifying the right visualizations of model output by applying ML to characteristics of the output or actions of the users.