Name
Title | ||
|---|---|---|
Efficient implementation of complex interventions in large scale epidemic simulations |
Abstract | ||
|---|---|---|
Realistic agent-based epidemic simulations usually involve a large scale social network containing individual details. The co-evolution of epidemic dynamics and human behavior requires the simulation systems to compute complex real-world interventions. Calls from public health policy makers for executing such simulation studies during a pandemic typically have tight deadlines. It is highly desirable to implement new interventions in existing high-performance epidemic simulations, with minimum development effort and limited performance degradation. Indemics is a database supported high-performance epidemic simulation framework, which enables complex intervention studies to be designed and executed within a short time. Unlike earlier approaches that implement new interventions inside the simulation engine, Indemics utilizes DBMS and reduces implementation effort from weeks to days. In this paper, we propose a methodology for modeling and predicting performance of Indemics-supported intervention studies. We demonstrate our methodology with experimental results. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/WSC.2011.6147856 | Winter Simulation Conference |
Keywords | Field | DocType |
medical information systems,health care,large scale epidemic simulations,efficient implementation,epidemic dynamic,epidemic dynamics,indemics-supported intervention study,realistic agent-based epidemic simulation,public health,indemics,new intervention,realistic agent-based epidemic simulations,dbms,complex intervention,simulation system,large scale epidemic simulation,high-performance epidemic simulation,epidemics,human behavior,high-performance epidemic simulation framework,simulation study,simulation engine,large scale social network,social networking (online),indemics utilizes dbms,social network,prediction model,predictive models,computational modeling,computer model,engines,computational complexity | Public health,Health care,Psychological intervention,Social network,Simulation,Computer science,Epidemic dynamics,Pandemic | Conference |
ISSN | ISBN | Citations |
0891-7736 E-ISBN : 978-1-4577-2107-6 | 978-1-4577-2107-6 | 2 |
PageRank | References | Authors |
0.40 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yifei Ma | 1 | 38 | 7.20 |
| Keith R. Bisset | 2 | 65 | 8.05 |
| Jiangzhuo Chen | 3 | 208 | 22.89 |
| Suruchi Deodhar | 4 | 17 | 3.21 |
| Madhav Marathe | 5 | 2775 | 262.17 |