attack of the mutant killer virus from se asia
DESCRIPTION
Attack of the Mutant Killer Virus from SE Asia. Swedish Institute for Infectious Disease Control, Karolinska Institutet, Stockholm University Martin Camitz Macro versus micro in epidemic simulations and other stories . Assault strategy. Macro vs. Micro. Realistic. Simple. - PowerPoint PPT PresentationTRANSCRIPT
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Swedish Institute for Infectious Disease Control,Karolinska Institutet,Stockholm University
Martin CamitzMacro versus micro in epidemic simulations and other
stories
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Assault strategy
MacroMacrovs.vs.
MicroMicro
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Simple Realistic
(Used without any permission whatsoever from A. Vespignani.)
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Simple Realistic
(Used without any permission whatsoever from A. Vespignani.)
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Dispersion
•Person to person–Residual viral mist
•Random mixing•Travel
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Our Travelrestrictions model
• Martin Camitz & Fredrik Liljeros, BMC Medicine, 4:32– Inspired by Hufnagel et al., PNAS, 2004
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Swedish travel network
• Survey data with 17000 respondents• 3 year sampling duration• 1 day sample • 60 days for long distance• 35000 intermunicipal trips
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SLIR-model
IS L R
3 events
•Number of infectious
•Infectiousness
•Incubation time •Recovery time
etc…
×289
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SLIR-model
IS L R
3 events
•Incubation time •Recovery time
in Solna
•Infectious in other municipalities
•Travel intensity
•Number of infectious
•Infectiousness
in Solna
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Dispersion equations
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1. Pick an event
QL QR
QL QI QR
QL QI
2. Pick a time step t
3. Update intensities
QIStockholm
4. Repeat from 1.
Kalmar
Solna
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Question
• What happens if we restrict travel?– Say longer journeys than 50 km or 20 km no
longer permitted.
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Restricting travel
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Restricting travel
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Our agent based micromodel
• Micropox to be published• Microsim under construction• With Lisa Brouwers at SMI + crew
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We have microdata on:• Age, sex, region…• Family• Workplace• Schools• Coordinates of all the above• Traveldata
– Improved aggregation for Microsim– More variables
• Duration• Traveling company• Business trip, vacation etc
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08.00
23.00
09.00
Working At home [unemployed, retired or ill]
Traveling Visiting the emergency room
Home for the night
08.00
DaytimeInfection all places
Day nEarly morning
NighttimeInfection at home
Day n+1Early morning
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Calibration
• Reasonable attack rate• A version of R0 calibrated on other
peoples version of R0• Expected place distribution of prevalence
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Place distribution of prevalence
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Results for Micropox
• Targeted vaccination of ER-personel in combination with ring vaccination (5.3)
superior to
• Mass vaccination (13.5)• Ring vaccination only (28.0)• ER-personell only (30.4)
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Microsim disease model
• Infectivity profile and susceptibility from Carat et al., 2006
• Certain other parameters from Ferguson, 2005– Latency time– Subsymptomatic infectiousness– Death rate
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Advantages
• We can model everything!
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Disadvantages
• We can model everything!
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Keep in mind that:
• ”All simulations are doomed to succeed.”- Rodney Brooks
• Strive to minimize assumptions• Comparative results only
– Possibly infer infectious disease parameters• Sensitivity analyses• Predictability
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We still have no clue
• Disease dynamics• Social behaviour
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Reviewers dream
• Did you take inte account…– the size of subway train compartments?– in Macedonia child care closes at 4pm?
• It’s Sweden– The general applicability is questionable.– Suggest using a Watts/Strogatz network
instead.
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Comparative results
• Is this a limitation?– Vaccination policies– Travel restrictions– School/workplace closing
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Output
• Incidence• Hospital load• Place distribution• Workforce reduction
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Still not convinced
• Steven Riley, Science, June 1– ”Detailed microsimulation models have not yet
been implemented at scales larger than a city.”
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Company network
• Real data of the Swedish population, workplaces and families
• Workplaces connected via the families of employees
• 500 000 nodes• 2 000 000 links
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• Weighted according to probability to transmit a disease
• Ex assign p=.5, the probability to transmit to/from family/workplace
• Yeilds weights (p), a probability to transmitt workplace to workplace.
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Company network
2.04
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Company network
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Breaking links vs nodes
• Don’t have to visit leaves.Leaves
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Breaking links vs nodes
• Don’t need to vaccinate the whole family.
Workplace
Family
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BackgroundZhenhua Wu, Lidia Braunstein, Shlomo Havlin, Eugene Stanley,
Transport in Weighted Networks: Partition into Superhighways and Roads, Physical Review Letters 96, 148702 (2006)
Random (ER) and scale free nets. Random weights.
Superhighways
Roads
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Method/Result
• Remove links, lowest weight first until percolation threshold (pc) by method.
• The remaining largest cluster (IIC-cluster) have a higher Betweeness Centrality than those of the Minimum Spanning Tree.
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Percolation threshold in workplace network
• ~200 distinct weights• Second largest cluster-method• Remove all same-weight links, lowest first,
plotting size of the second largest cluster• Maximum => pc
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Community structure
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Modularity
• M <= 0• M = 0 for random graphs
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Maximizing M
• Newman/Girvan• Simulated annealing• Greedy method
– New one by Aaron Clauset for large networks
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Hub clusters
• Fix number of modules to 2 (or ~10).• Fix number of nodes in all but one module
to n=100.• Minimize M• Then increase n in increments of 100.