SC Conference - Activity Details

EpiSimdemics: An Efficient Algorithm for Simulating the Spread of Infectious Disease over Large Realistic Social Contact Networks

Chris L. Barrett  (Virginia Tech)
Keith R. Bisset  (Virginia Tech)
Stephen G. Eubank  (Virginia Tech)
Xizhou Feng  (Virginia Tech)
Madhav V. Marathe  (Virginia Tech)
Papers Session
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
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