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

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

5. Simple Realistic (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. SLIR-model I SLR 3 events Number of infectious Infectiousness Incubation timeRecovery time etc… ×289

11. SLIR-model I SLR 3 events Incubation timeRecovery time in Solna Infectious in other municipalities Travel intensity Number of infectious Infectiousness in Solna

12. Dispersion equations

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

14. Run it on a really big PC

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

16. Restricting travel

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

19. 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

20. 08.00 23.00 09.00 WorkingAt home [unemployed, retired or ill] TravelingVisiting the emergency room Home for the night 08.00 Daytime Infection all places Day n Early morning Nighttime Infection at home Day n+1 Early morning

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

22. Place distribution of prevalence

23. 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)

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

25. Advantages We can model everything!

26. Disadvantages We can model everything!

27. 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

28. We still have no clue Disease dynamics Social behaviour

29. 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.

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

31. Output Incidence Hospital load Place distribution Workforce reduction

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

33. 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

34. 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.

35. Company network 2.04

36. Company network

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

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

39. 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

40. Method/Result Remove links, lowest weight first until percolation threshold (p c ) by  method. The remaining largest cluster (IIC-cluster) have a higher Betweeness Centrality than those of the Minimum Spanning Tree.

41. 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 => p c

43. Community structure

44. Modularity M <= 0 M = 0 for random graphs

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

46. 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.