epifast: a fast algorithm for large scale realistic epidemic simulations on distributed memory...
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EpiFast: A Fast Algorithm for Large Scale Realistic Epidemic
Simulations on Distributed Memory Systems
Keith R. Bisset, Jiangzhuo Chen, Xizhou Feng, V.S. Anil Kumar, Madhav V. Marathe
Network Dynamics & Simulation Science Laboratory
23rd International Conference on Supercomputing (ICS'09)
June 11, 2009
Network Dynamics & Simulation Science Laboratory
Outline
• Background• EpiFast Algorithm• Performance• Summary
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Motivation
• Pandemic Flu of 1918 was deadly– Killed 2.5 - 5% of global population– Many many more were sick– Resulted in massive upheaval of
society– Virtually no place on Earth was
spared• More recently:
– SARS– Avian influenza– Swine flu
• Epidemic simulation problem
Network Dynamics & Simulation Science Laboratory
Network Dynamics & Simulation Science Laboratory
Components of Epidemic Simulation Problem
• Population and contact network• Infectious disease• Interventions
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Create a Synthetic Population
• Census data– Individual demographics: age, gender…– Household characteristics: size, income…
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Generate Contact Network
• Locations: D&B data• Activity surveys.
– Matched to individuals by demographics– Matched to locations by activity type
• Generate social contact network– People follow activity schedules– Activities take them to locations– At locations they interact with each other
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Generate Contact Network
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Social Contact Network
• All interactions in population captured– Duration of contact– Type of activity resulting in contact– Demographics of those contacted– Characteristics of locations
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Social Contact Network
• Interactions provide opportunity for disease transmission
• All interactions in a population can get very complex
• Eg. Alabama has 4.3 million people and a total of 291 million interactions
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Background: SEIR Disease Model
• Individuals move through states with different characteristics
• Demographics• Level of symptoms• Level of infectiousness• Response to treatments
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Disease Spread in Contact Network
• Transmission depends on– Duration of contact– Type of contact– Characteristics of the infectious person– Characteristics of the susceptible person
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Background: Interventions
• Different types of interventions help to mitigate the epidemic– Pharmaceutical: vaccination, antiviral– Non-Pharmaceutical: social distancing, school closure,
work closure
• When, how, and to whom these are applied can have different impact on the course of the epidemic
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Obstacles to Interventions
• Supply: many interventions are of a limited supply thus only a fraction of the population may be eligible for the intervention
• Compliance: not all individuals will be able or willing to comply with the intervention
• Efficacy: not all interventions are fully effective even if complied with
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Vaccination
• Vaccination changes an individual’s role in the transmission chain – Lowers susceptibility to infection – Lowers infectiousness if infected
• The degree these are lowered depends on the efficacy of the vaccine
• Predicted efficacies and supply levels of pandemic flu vaccines vary wildly
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Antiviral
• Anti-viral treatment changes a individual’s role in the transmission chain for the duration of their treatment– Lowers susceptibility to infection– Lowers infectiousness if infected
• The efficacies of these treatments depends on:– The kind of anti-viral administered– When its administered
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Social Distancing
• Generic Social Distancing reduces the opportunities for transmission in the population– Less contact at public places
• Either through closures or rules on occupancy
– Measures that might reduce transmission• Masks, no hand shaking, frequent sterilization of common
surfaces
• The degree to which this occurs depends mainly on compliance
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School Closure
• School closure reduces opportunities for transmission at schools– School children are often involved in the early spread
of influenza epidemics
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Work Closure
• Work closures eliminate the opportunities for transmission within the workplace– Workplaces close their doors
• The degree this will work will depend on the compliance levels of businesses
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Application of Interventions
The effectiveness of all interventions depend on when, how, and to whom they are applied
• When is it triggered? – An event triggers the implementation of the intervention
(day of simulation or % of a group is infected)
• How well is the plan executed?– What proportion of the targeted population actually
received / complied with the intervention (levels of compliance)
• Who was targeted?– Supply limitations may require prioritization of groups for
different interventions
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EpiFast Algorithm: Sequential
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Parallelization
• Data intensive & computation intensive.• Should scale on distributed memory systems.• Partition data (contact network).• Master-slave model.
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Parallel EpiFast: Network Partitioning
A
B
E
C
D
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Parallel EpiFast: Master-Slave Model
• Single master processor: communication
talk the talk• Many slave
processors: computation
work the work
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EpiFast Algorithm: Parallel
Sequential:
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Network Dynamics & Simulation Science Laboratory
EpiFast Performance: Running Time
• C++/MPI implementation, tested on commodity clusters and SGI Altix systems.
• Los Angeles population: 16 million people.• 180 days of epidemic duration.• With and without interventions.• 25 replicates for each configuration.• Each replicate takes < 15 minutes.
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EpiFast Performance: Running Time
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EpiFast Performance: Strong Scaling
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EpiFast Performance: Week Scaling
Population Population Size CPU Number Running Time (seconds)per simulation day
Miami 2.09 32 0.47
Boston 4.15 64 0.54
Chicago 9.05 128 0.54
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Network Partitioning Revisited
• Our simple partitioning method is scalable.
• Can be done online with very little time: adjust partitioning based on available computing resource to achieve load balancing.
• Metis produces better partitioning: slightly improves communication complexity, with a significant overhead.
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Summary
EpiFast:• can handle realistic large scale populations;• has many practical applications: evaluation of
various interventions, public health decision support;
• runs extremely fast;• is scalable: on both shared & distributed memory
systems.• Is a novel HPC application: epidemic simulation.
Thanks!
Network Dynamics & Simulation Science Laboratory
Future Work
• Implement EpiFast with UPC.• Port EpiFast to GPGPU or Cell based clusters.
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