Identification

Author

Bisset KR, Chen J, Feng X, Kumar VSA, Marathe MV

Title

EpiFast: A fast algorithm for large scale realistic epidemic simulations on distributed memory system

Year

2009

Publication type

Proceedings

Journal

Proceedings of the 23rd international conference on Supercomputing

Created

2014-04-07 21:04:56+00:00

Modified

2016-07-27 17:04:02.179767+00:00

Details

Pages

430-439

Access

Language

English

URL http://staff.vbi.vt.edu/chenj/pub/2009_ICS.pdf
DOI

10.1145/1542275.1542336

Accessed

2016-06-28

Extended information

Abstract

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems.

EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results.

EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.