Indemics: An interactive high-performance computing framework for data-intensive epidemic modeling

Authors:
Keith R. Bisset;Jiangzhuo Chen;Suruchi Deodhar;Xizhou Feng;Yifei Ma;Madhav V. Marathe
Affiliations:
NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA;NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA;NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Department of Computer Science, Virginia Tech, Blacksburg, VA;NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Department of Mathematics, Statistics, and Computer Science, Marquette University, Blacksburg, VA;NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Department of Computer Science, Virginia Tech, Blacksburg, VA;NDSSL, Virginia Bioinformatics Institute, Virginia Tech, Department of Computer Science, Virginia Tech, Blacksburg, VA
Venue:
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
Year:
2014

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Abstract

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented.