GLUTATHIONE TRANSFERASES Ralf Morgenstern Institute of Environmental Medicine Karolinska Institutet.
Swedish Institute for Infectious Disease Control, Karolinska Institutet, Stockholm University Martin...
-
Upload
cecil-flowers -
Category
Documents
-
view
220 -
download
0
Transcript of Swedish Institute for Infectious Disease Control, Karolinska Institutet, Stockholm University Martin...
Swedish Institute for Infectious Disease Control,
Karolinska Institutet,
Stockholm University
Martin CamitzMacro versus micro in epidemic simulations and other
stories
Assault strategy
MacroMacrovs.vs.
MicroMicro
Simple Realistic
(Used without any permission whatsoever from A. Vespignani.)
Simple Realistic
(Used without any permission whatsoever from A. Vespignani.)
Dispersion
•Person to person–Residual viral mist
•Random mixing
•Travel
Our Travelrestrictions model
• Martin Camitz & Fredrik Liljeros, BMC Medicine, 4:32– Inspired by Hufnagel et al., PNAS, 2004
Swedish travel network
• Survey data with 17000 respondents
• 3 year sampling duration
• 1 day sample
• 60 days for long distance
• 35000 intermunicipal trips
SLIR-model
IS L R
3 events
•Number of infectious
•Infectiousness
•Incubation time •Recovery time
etc…
×289
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
Dispersion equations
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
Question
• What happens if we restrict travel?– Say longer journeys than 50 km or 20 km no
longer permitted.
Restricting travel
Restricting travel
Our agent based micromodel
• Micropox to be published
• Microsim under construction
• With Lisa Brouwers at SMI + crew
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
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
Calibration
• Reasonable attack rate
• A version of R0 calibrated on other peoples version of R0
• Expected place distribution of prevalence
Place distribution of prevalence
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)
Microsim disease model
• Infectivity profile and susceptibility from Carat et al., 2006
• Certain other parameters from Ferguson, 2005– Latency time– Subsymptomatic infectiousness– Death rate
Advantages
• We can model everything!
Disadvantages
• We can model everything!
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
We still have no clue
• Disease dynamics
• Social behaviour
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.
Comparative results
• Is this a limitation?– Vaccination policies– Travel restrictions– School/workplace closing
Output
• Incidence
• Hospital load
• Place distribution
• Workforce reduction
Still not convinced
• Steven Riley, Science, June 1– ”Detailed microsimulation models have not yet
been implemented at scales larger than a city.”
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
• 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.
Company network
2.04
Company network
Breaking links vs nodes
• Don’t have to visit leaves.Leaves
Breaking links vs nodes
• Don’t need to vaccinate the whole family.
Workplace
Family
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
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.
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
Community structure
Modularity
• M <= 0
• M = 0 for random graphs
Maximizing M
• Newman/Girvan
• Simulated annealing
• Greedy method– New one by Aaron Clauset for large networks
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.