ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2009
Assumptions management in simulation of infectious disease outbreaks.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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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
5 citing papers in PubMed, 12 citations in OpenAlex.
- Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures.Epidemiology and infection · 2016Article
- Dynamic Multicore Processing for Pandemic Influenza Simulation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Article
- A cloud-based simulation architecture for pandemic influenza simulation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
- A neighborhood susceptibility index for planning of local physical interventions in response to pandemic influenza outbreaks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simulation of outbreaks of infectious disease is an important tool for understanding the dynamics of the outbreak process, the impact of disease and population properties, and the potential effect of interventions. However, the interpretation of the simulation results requires a clear understanding of the assumptions made in the underlying model. Typical simulation tasks, such as exploring the space of different scenarios for population and disease properties, require multiple runs with varying model parameters. For such complex tasks, the management of the assumptions made becomes a daunting and potentially error-prone undertaking. We report explicit assumptions management as an approach to capture, model, and document the assumptions for simulator runs. It was found possible to extend ontology-based simulation, which uses an ontological model to parameterize the simulator, to incorporate an assumptions model in the ontology. We conclude that explicit assumptions modeling should be part of any infectious disease simulation architecture from start.
Indexed as
Identifiers
20351844PMC2815436W37357378What OpenQuestion holds
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.