Attack of the Mutant Killer Virus from SE Asia

1. Attack of the Mutant Killer Virus from SE Asia

2. Swedish Institute for Infectious Disease Control, Karolinska Institutet, Stockholm University Martin Camitz Macro versus micro in epidemic simulations and other stories

3. Assault strategy Macrovs.Micro

4. Realistic Simple (Used without any permission whatsoever from A. Vespignani.)

5. Realistic Simple (Used without any permission whatsoever from A. Vespignani.)

6. Dispersion Person to person Residual viral mist Random mixing Travel

7. Our Travelrestrictions model • Martin Camitz & Fredrik Liljeros, BMC Medicine, 4:32 • Inspired by Hufnagel et al., PNAS, 2004

9. Swedish travel network • Survey data with 17000 respondents • 3 year sampling duration • 1 day sample • 60 days for long distance • 35000 intermunicipal trips

10. L S I R SLIR-model etc… 3 events ×289 • Number of infectious • Infectiousness • Incubation time • Recovery time

11. L S I R SLIR-model in Solna 3 events • Number of infectious • Infectiousness • Incubation time • Recovery time in Solna • Infectious in other municipalities • Travel intensity

12. Dispersion equations

13. I Q 1. Pick an event Stockholm L R Q Q 2. Pick a time step Dt Kalmar L I R Q Q Q Solna L I Q Q 3. Update intensities 4. Repeat from 1.

14. Question • What happens if we restrict travel? • Say longer journeys than 50 km or 20 km no longer permitted.

15. Restricting travel

16. Restricting travel

17. Our agent based micromodel • Micropox to be published • Microsim under construction • With Lisa Brouwers at SMI + crew

18. 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

19. Day n Early morning Day n+1 Early morning Daytime Infection all places Nighttime Infection at home 08.00 09.00 Working At home [unemployed, retired or ill] Traveling Visiting the emergency room 23.00 Home for the night 08.00

20. Calibration • Reasonable attack rate • A version of R0 calibrated on other peoples version of R0 • Expected place distribution of prevalence

21. Place distribution of prevalence

22. 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)

23. Microsim disease model • Infectivity profile and susceptibility from Carat et al., 2006 • Certain other parameters from Ferguson, 2005 • Latency time • Subsymptomatic infectiousness • Death rate

24. Advantages • We can model everything!

25. Disadvantages • We can model everything!

26. 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

27. We still have no clue • Disease dynamics • Social behaviour

28. 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.

29. Comparative results • Is this a limitation? • Vaccination policies • Travel restrictions • School/workplace closing

30. Output • Incidence • Hospital load • Place distribution • Workforce reduction

31. Still not convinced • Steven Riley, Science, June 1 • ”Detailed microsimulation models have not yet been implemented at scales larger than a city.”

32. 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

33. Weighted according to probability to transmit a disease • Ex assign p=.5, the probability to transmit to/from family/workplace • Yeilds weights p(p), a probability to transmitt workplace to workplace.

34. Company network 2.04

35. Company network

36. Breaking links vs nodes • Don’t have to visit leaves. Leaves

37. Breaking links vs nodes • Don’t need to vaccinate the whole family. Family Workplace

38. Background Zhenhua 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

39. Method/Result • Remove links, lowest weight first until percolation threshold (pc) by k-method. • The remaining largest cluster (IIC-cluster) have a higher Betweeness Centrality than those of the Minimum Spanning Tree.

40. 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

42. Community structure

43. Modularity • M <= 0 • M = 0 for random graphs

44. Maximizing M • Newman/Girvan • Simulated annealing • Greedy method • New one by Aaron Clauset for large networks

45. 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.