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

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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

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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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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!

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Future Work

• Implement EpiFast with UPC.• Port EpiFast to GPGPU or Cell based clusters.