Talk at the International Conference on Computational Social Science, Helsinki, June 9, 2015. On YouTube here (Plenary II): https://www.youtube.com/channel/UCUGsbLwL4G2CQQfk95oZjVw

2. A B
C

3. ‘40s
Timeline
1947 First programmable computer
1948 The first simulation study, the Monte Carlo project

4. ‘40s
Timeline
1947 First programmable computer
1948 The first simulation study, the Monte Carlo project
‘50s
Timeline
1952 The first computational epidemiology study:
H Abbey. An examination of the Reed-Frost theory
of epidemics. Hum. Biol. 24: 201–233.
‘70s
Timeline
1978 The core group concept:
JA Yorke, HW Hethcote, A Nold. Dynamics and control of
the transmission of Gonorrhea. Sex. Transm. Dis. 5: 51–56.

5. Timeline
1984 Birth of network epidemiology:
DM Auerbach, WW Darrow, HW Jaffe, JW Curran, Cluster of
cases of the acquired immune deficiency syndrome:
Patients linked by sexual contact. Am. J. Med. 76: 487–492.
‘80s

6. ‘40s
Timeline
1947 First programmable computer
1948 The first simulation study, the Monte Carlo project
‘50s
Timeline
1952 The first computational epidemiology study:
H Abbey. An examination of the Reed-Frost theory
of epidemics. Hum. Biol. 24: 201–233.
‘70s
Timeline
1978 The core group concept:
JA Yorke, HW Hethcote, A Nold. Dynamics and control of
the transmission of Gonorrhea. Sex. Transm. Dis. 5: 51–56.
1984 Birth of network epidemiology:
DM Auerbach, WW Darrow, HW Jaffe, JW Curran, Cluster of
cases of the acquired immune deficiency syndrome:
Patients linked by sexual contact. Am. J. Med. 76: 487–492.
‘80s
Timeline
1995 Birth of computational network epidemiology:
M Kretzschmar. Deterministic and stochastic pair
formation models for the spread of sexually transmitted
diseases. J. Biol. Syst. 3: 789–801.
‘90s

7. Modeling
Step 1: Compartmental models
Susceptible
meets
Infectious
Infectious
With some probability or rate
Susceptible or
Recovered
With some rate or after some time

8. Modeling
Step 2: Contact patterns
time

9. The core group idea
Core groups bring a
population over an
epidemic threshold,
even though it, on
average, wouldn’t be.
Being a member of a core
group = being important
for the disease. But any
individual in the core
group is insignificant for
the core group.
Theparadox

10. Which one depend on
outbreak scenarios and
intervention scenarios.
Many facets of importance

11. Which one depend on
outbreak scenarios and
intervention scenarios.
Many facets of importance

12. The hypotheses

13. The hypotheses
Core groups can be captured by static network structure.
Structure & dynamics can be coupled by the SIS survival time.
For many vaccinees, the core group would be most important.

14. The hypotheses
For few vaccinees, hubs would be most important . . .

15. The hypotheses
. . . or bridges.

16. The hypotheses
Core groups can be captured by static network structure.
Structure & dynamics can be coupled by the SIS survival time.
Vaccination-impact very correlated with degree.

17. Less paradoxical paradox
n = 0 n = 1
n = 2 n = 3

18. We get out of the cloud sometimes . . .

19. Temporal networks
P Holme, J Saramäki, 2012. Phys. Rep. 519: 97–125.
P Holme, J Saramäki, 2013. Temporal Networks. Berlin, Springer.

20. … of human interaction
J Saramäki & al., 2013. PNAS 111: 942–947.
LEC Rocha, F Liljeros, P Holme, 2010. PNAS 107: 5706–5711.
LEC Rocha, F Liljeros, P Holme, 2011. PLoS Comp. Biol. 7: e1001109.
M Karsai, J Saramäki & al. Phys. Rev. E 83: 025102.
P Holme, 2005. Phys. Rev. E 71: 046119.

21. Optimal static networks from
temporal network data
P Holme, 2013. PLoS. Comp. Biol. 9: e1003142.
Time-window networks good,
but be careful with the window size.

22. Simplified pictures of temporal
networks
P Holme, F Liljeros, 2014.
Sci. Rep. 4: 4999.
time
Beginning &
end of
relationships
more
important
than,
interevent
times for SIR
on empirical
data.

23. T H A N
K Y O U
Illustrations:
Mi Jin Lee