1525061, #1524972, and #1525012

VIRAL INFECTION PROPAGATION THROUGH AIR TRAVEL
PIs: S. Namilae, M. Scotch, R. Pahle, and A. Srinivasan Collaborators: A. Mubayi and C.D. Sudheer HUMAN MOVEMENT MODEL BACKGROUND v  Air travel is a major cause of the spread of infecOons v  This led to calls for ban on air travel during the Ebola outbreak v  Such bans have serious human and economic consequences v  Can fine-­‐tuned policy prescripOons provide the same benefit without the negaOve consequences? v  Current models for spread of infecOons through air-­‐
travel use aggregate passenger traffic v  Makes it difficult to predict consequences of policies that change human interacOon paWerns v  PredicOon is difficult due to inherent uncertainOes and absence of much data with new outbreaks PROJECT GOALS v  Develop a decision support infrastructure that will analyze the impact of new procedures or policies on spread of Ebola during flights v  Will answer quesOons such as the following v  Self Propelled EnOty Dynamics (SPED) model v  Based on social dynamics v  Each enOty is treated as a parOcle v  Humans experience self-­‐propulsion, which moves them toward their goal v  They experience repulsive forces from other humans and surfaces v  Forces are computed based on potenOals, as in molecular dynamics v  Passenger trajectories are obtained by solving the standard differenOal equaOon for moOon v  We add human behavioral features to get a realisOc model v  Behavioral features and the potenOals include parameters that can be varied to produce changes in human movement paWerns v  How high a risk does air travel pose in spreading Ebola beyond its source countries? v  Can simple changes in procedures reduce the risk of infecOon spread without causing major disrupOons? v  Change boarding and disembarkaOon procedures v  Change seaOng arrangements v  Change plane type Workflow for passenger movement model 3624
METHODOLOGY Baseline
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v  Leverages prior experOse in human movement modeling, parallel compuOng, infecOon modeling, phylogeography, and decision support so]ware 1642
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SimulaOon Ome distribuOon on 68921 cores before (le]) and a]er (right) IO opOmizaOon on Bluewaters at NCSA. Cores vs Time
Time is predicted using results from 1331 cores with a piecewise quadraOc curve fit. A factor 11/9 approximaOon algorithm for bin-­‐packing is used to give Omes on different numbers of cores. 2400
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Le]: Load balancing for 68921 parameters using 39655 cores. Dynamic load balancing uses a factor 2 approximaOon for minimum makespan. ‘Dynamic sorted’ uses dynamic load balancing a]er sorOng tasks by decreasing order of predicted Omes. If Omes are exact, then this is a factor 4/3 approximaOon. StaOc load balancing uses predicted Omes with the factor 1.22 MulOfit approximaOon algorithm. Right: If we have exact Omes, then staOc load balancing can yield almost opOmum Ome using 32109 cores. 1624
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v  IdenOfy vulnerabiliOes through parameter sweep of this space of uncertainOes v  High computaOonal cost arises from the large parameter space v  Explore this space efficiently v  Efficient parallelizaOon to reduce simulaOon Ome Alternate Columns
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v  Use a fine-­‐scale model that tracks movement of individual passengers in a plane v  Link with a phylogeography model, which uses geneOc and geographic informaOon, to analyze global consequences v  Parameterize sources of uncertainty www.cs.fsu.edu/vipra B 7 5 7 Threshold=30 in
Impact of different deplaning strategies on contacts in a 144 seat Airbus 320 and 182 seat Boeing 757-­‐200, using different thresholds for contacts. Ebola is transmiWed by contact, and we desire to reduce the number of contacts. PHYLOGEOGRAPHY v  We analyzed the phylogeography of the 2014 Ebola outbreak in West Africa v  It considers more sequences and locaOons than prior work v  Guinea was the likely source of the outbreak v  There was no difference in virus persistence among the African countries v  The evoluOon of the Zaire Ebola virus is structured by geography COMPUTATION v  Around factor 10 improvement in performance through opOmizaOons v  OpOmizaOon of sequenOal performance v  OpOmizaOon of workflow – some parts of validaOon were integrated with the simulaOon, which led to early terminaOon of some long runs that did not yield realisOc passenger behavior v  OpOmizaOon of parallel IO reduced Luster metadata read overhead v  Dynamic load balancing increases parallel efficiency from 51% to 81 % with liWle increase in Ome v  Plaqorm: Bluewater XE nodes, 32 processes per node v  BeWer Ome predicOon can improve efficiency further Pairwise migraOon rate between countries. The rates are in terms of lineages per year, with thicker lines indicaOng greater rates. The higher rates to Europe (for example, 14.6 for Sierra Leone to Switzerland) are impacted by the small number of sequences. v  IdenOfied procedures that can lead to significant decrease in contacts v  Order of magnitude improvement in performance through opOmizaOons, with load balancing reducing the number of nodes needed with minimal increase in Ome v  Showed that evoluOon of the Zaire Ebola virus was geographically structured v  Future work v  Link models using ChainBuilder based decision support system v  Reduce simulaOon Ome by parallelizing individual simulaOons, exploring the parameter space more efficiently, and beWer load balancing v  Extend the work to air-­‐borne diseases v  Extend the work to airports, in order to cover the enOre air-­‐
travel system REFERENCES v  A. Srinivasan, C.D. Sudheer, and S. Namilae. Op#mizing Massively Parallel Simula#ons of Infec#on Spread Through Air-­‐Travel for Policy Analysis. CCGrid 2016. v  M. Scotch, R. Beard, R. Pahle, A. Mubayi, S. Namilae, and A. Srinivasan. The Spread of the 2014 Ebola Zaire Virus in West Africa. PSB 2016 (poster). v  PresentaOons on Project VIPRA at: v  Daytona Beach InternaOonal Airport Emergency Support FuncOon Partners MeeOng, Florida Department of Health in Volusia County v  Talk at SC ‘15, NCSA Exhibit v  Invited talk at InternaOonal Symposium on ComputaOonal Science ACKNOWLEDGEMENTS This material is based upon work supported by the NaOonal Science FoundaOon ACI under grants #1525061, #1524972, and #1525012 (Simula'on-­‐Based Policy Analysis to Reduce Ebola Transmission Risk in Air Travel). Any opinion, findings, and conclusions or recommendaOons expressed in this material are those of the authors and do not necessarily reflect the views of the NaOonal Science FoundaOon. We thank NCSA for providing use of the Bluewaters supercomputer.