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Disease spreading & control in temporal networks

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A presentation about this paper:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0036439

Publié dans : Technologie
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Disease spreading & control in temporal networks

  1. 1. Disease spreading & control in temporal networks Petter Holme, Umeå University & SKKU in KAIST Dec 14, 2010 with Luis EC Rocha, Sungmin Lee, Fredrik Liljeros
  2. 2. Temporal structures 6,7,9 B C 1,2,4,5 11 10,15 A time
  3. 3. What we are interested in • How does temporal structures in empirical networks affect disease spreading? • Can we exploit these structures to slow down disease spreading?
  4. 4. Our datasets • E-mail: 3,188 nodes, 309,125 contacts over 83 days • Internet dating: 29,341 nodes, 536,276 contacts over 512 d • Hospital: 295,107 nodes, 64,625,283 contacts over 8,521 d • Prostitution: 16,730 nodes, 50,632 contacts
  5. 5. Avg. max speed vs. 0- model
  6. 6. SI model, vs ρ = 1
  7. 7. Outbreak diversity
  8. 8. Threshold in transmissivity
  9. 9. Threshold in duration
  10. 10. Contact seq vs other models
  11. 11. HIV, two-stage model
  12. 12. A society-wide context
  13. 13. Summary • Temporal correlations speed up the outbreaks on a short time scale & slows it down on a longer time scale • Temporal effects create distinct and comparatively high epidemic thresholds • HIV can not spread in the prostitution data alone and probably does not serve as a reservoir of HIV in a society-wide
  14. 14. Temporal vaccination
  15. 15. Relative effic., worst case f (%) f (%) f (%) 20 10 0 –10 –20 2 0 –2 –4 0 20 40 60 80 0 20 40 60 80 1 0 –1 –2 2 –3 20 10 0 –10 –20 0 20 40 60 80 0 20 40 60 80 f (%) D E-mailC Hospital B Internet datingA Prostitution ΔS(%) Weight Recent ΔS(%) ΔS(%) ΔS(%)
  16. 16. Relative effic., SIR Δs(%) 0.2 0.1 0 –0.1 –0.2 –0.3 Δs(%) 10 5 0 –5 –10 –15 f (%) 0 20 40 60 80 0 20 40 60 80 f (%) f (%) 0 20 40 60 80 0 20 40 60 80 f (%) 40 20 0 –20 10 5 0 –5 –10 –15 Δs(%) Δs(%) B Internet dating C Hospital D E-mail A Prostitution Weight Recent
  17. 17. Parameter dep. rel. effic.
  18. 18. Explanatory model
  19. 19. Summary • Temporal correlations do affect disease spreading and can be exploited in targeted vaccination • The best vaccination strategy depends on the type of temporal structure • Until more structural information is available, we recommend the strategy Recent
  20. 20. deadline March 10 March 28 – April 20 nordita.org/network2011

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