SC Conference - Activity Details
EpiSimdemics: An Efficient Algorithm for Simulating the Spread of Infectious Disease over Large Realistic Social Contact Networks
Authors:| Chris L. Barrett (Virginia Tech) |
| Keith R. Bisset (Virginia Tech) |
| Stephen G. Eubank (Virginia Tech) |
| Xizhou Feng (Virginia Tech) |
| Madhav V. Marathe (Virginia Tech) |
| Biomedical Informatics |
| Wednesday, 04:30PM - 05:00PM |
| Room Ballroom E |
Preventing and controlling outbreaks of infectious diseases such as
pandemic influenza is a top public health priority. We describe
EpiSimdemics -- a highly scalable, parallel algorithm to simulate the
spread of contagion in large, realistic social contact networks using
individual-based models. EpiSimdemics is a discrete event simulation
of a certain class of stochastic reaction-diffusion
processes. Straightforward simulations of such process do not scale
well, limiting the use of individual-based models to very small
populations. EpiSimdemics is specifically designed to scale to social
networks with 100 million individuals. The scaling is obtained by
exploiting the semantics of disease evolution and disease propagation
in large networks. We evaluate an MPI-based parallel implementation
of EpiSimdemics on a mid-sized HPC system, demonstrating that
EpiSimdemics scales well. EpiSimdemics has been used in numerous
sponsor defined case studies aimed at policy planning and course of
action analysis, demonstrating the usefulness of EpiSimdemics in
practical situations.
The full paper can be found in the
IEEE Xplore Digital Library
and
ACM Digital Library
| IEEE Computer Society / ACM | 2 0 Y E A R S - U N L E A S H I N G T H E P O W E R O F H P C |