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

Network Dynamics & Simulation Science Laboratory Motivation Pandemic Flu of 1918 was deadly –Killed % 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

Network Dynamics & Simulation Science Laboratory Create a Synthetic Population Census data –Individual demographics: age, gender… –Household characteristics: size, income…

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory Generate Contact Network

Network Dynamics & Simulation Science Laboratory Social Contact Network All interactions in population captured –Duration of contact –Type of activity resulting in contact –Demographics of those contacted –Characteristics of locations

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory Background: SEIR Disease Model Individuals move through states with different characteristics Demographics Level of symptoms Level of infectiousness Response to treatments

Network Dynamics & Simulation Science Laboratory Disease Spread in Contact Network Transmission depends on –Duration of contact –Type of contact –Characteristics of the infectious person –Characteristics of the susceptible person

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory School Closure School closure reduces opportunities for transmission at schools –School children are often involved in the early spread of influenza epidemics

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory 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

Network Dynamics & Simulation Science Laboratory EpiFast Algorithm: Sequential

Network Dynamics & Simulation Science Laboratory Parallelization Data intensive & computation intensive. Should scale on distributed memory systems. Partition data (contact network). Master-slave model.

Network Dynamics & Simulation Science Laboratory Parallel EpiFast: Network Partitioning A B E C D

Network Dynamics & Simulation Science Laboratory Parallel EpiFast: Master-Slave Model Single master processor: communication talk the talk Many slave processors: computation work the work

Network Dynamics & Simulation Science Laboratory 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.

Network Dynamics & Simulation Science Laboratory EpiFast Performance: Running Time

Network Dynamics & Simulation Science Laboratory EpiFast Performance: Strong Scaling

Network Dynamics & Simulation Science Laboratory EpiFast Performance: Week Scaling PopulationPopulation SizeCPU NumberRunning Time (seconds) per simulation day Miami Boston Chicago

Network Dynamics & Simulation Science Laboratory 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.

Network Dynamics & Simulation Science Laboratory 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.