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A paradox of importance in network epidemiology

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

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

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A paradox of importance in network epidemiology

  1. 1. A B C
  2. 2. ‘40s Timeline 1947 First programmable computer 1948 The first simulation study, the Monte Carlo project
  3. 3. ‘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.
  4. 4. 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
  5. 5. ‘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
  6. 6. Modeling Step 1: Compartmental models Susceptible meets Infectious Infectious With some probability or rate Susceptible or Recovered With some rate or after some time
  7. 7. Modeling Step 2: Contact patterns time
  8. 8. 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
  9. 9. Which one depend on outbreak scenarios and intervention scenarios. Many facets of importance
  10. 10. Which one depend on outbreak scenarios and intervention scenarios. Many facets of importance
  11. 11. The hypotheses
  12. 12. 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.
  13. 13. The hypotheses For few vaccinees, hubs would be most important . . .
  14. 14. The hypotheses . . . or bridges.
  15. 15. 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.
  16. 16. Less paradoxical paradox n = 0 n = 1 n = 2 n = 3
  17. 17. We get out of the cloud sometimes . . .
  18. 18. Temporal networks P Holme, J Saramäki, 2012. Phys. Rep. 519: 97–125. P Holme, J Saramäki, 2013. Temporal Networks. Berlin, Springer.
  19. 19. … 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.
  20. 20. 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.
  21. 21. 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.
  22. 22. T H A N K Y O U Illustrations: Mi Jin Lee

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