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Corresponding to Ueno and Masuda, Journal of Theoretical Biology, 254, 655-666 (2008), which is located at
http://www.sciencedirect.com/science/article/pii/S0022519308003512

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UenoMasuda2008JTB-slide

  1. 1. Controlling nosocomial infection based on structure of hospital social networks Taro Ueno1,2, Naoki Masuda3,4 1 Institute of Molecular Embryology and Genetics, The University of Kumamoto 2 Tokyo Metropolitan Hiroo General Hospital 3 Graduate School of Information Science and Technology, The University of Tokyo 4 JST Presto Ref: J. Theor. Biol., 254, 655-666 (2008).
  2. 2. Objective of this study•Control nosocomial infections •By changing structure of hospital social networks •By vaccinating some individuals beforehand•What is special about that? •Hierarchical network structure •Hospital specific antibiotics-resistant pathogens (e.g. MRSA, VRE). •Severe results in immuno-deficient patients •As initiator or booster of epidemic outbreaks at urban community and world-wide levels
  3. 3. Social networks of a hospital• Data from a community hospital in Tokyo, Japan• Nodes [頂点] • (Hospitalized) Patient (Pt) [入院患者] • Nurse (Ns) [看護師] • (Medical) doctors (Dr) [医師]• Links [枝] • Pt-Ns, Pt-Dr, Ns-Dr, Dr-Dr ← medical record [カルテ] • (Dr-Dr ← same team) • Pt-Pt ← same room [同じ病室] • Ns-Ns ← same ward [同じ病棟]
  4. 4. Def of contact networkmedical record network medical doctor [医師] nurse [看護師] hospitalized patients [入院患者]
  5. 5. Hierarchical Structure [ネットワークの階層構造] team department [診療科]room [病室]ward [病棟] Dr [医師] Ns [看護師] Pt [入院患者]
  6. 6. Visualization 605 nodes 605$–$42$ persons’$ 3046 links networkDr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  7. 7. Visualization 605 nodes 605$–$42$ persons’$ 3046 links networkDr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  8. 8. Visualization 605 nodes 605$–$42$ persons’$ 3046 links networkDr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  9. 9. Visualization 605 nodes 605$–$42$ persons’$ 3046 links networkDr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388
  10. 10. Visualization 605 nodes 605$–$42$ persons’$ 3046 links networkDr [医師] 123 Ns [看護師] 94 Pt [入院患者] 388 Drs are across-ward transmitters?
  11. 11. Network Properties characteristic path length clustering coefficient hospital network 4.39 0.53randomized network 3.02 0.029
  12. 12. Network Properties characteristic path length clustering coefficient hospital network 4.39 0.53randomized network 3.02 0.029 Small-world and hierarchical
  13. 13. Degree Distributionheterogeneous, not really scale-free (too small N)
  14. 14. SIR Model
  15. 15. SIR ModelSusceptible S
  16. 16. SIR ModelSusceptible S I Infected
  17. 17. SIR ModelSusceptible S I Infected
  18. 18. SIR ModelSusceptible S
  19. 19. SIR ModelSusceptible λ: infection rate S
  20. 20. SIR ModelSusceptible Infected λ: infection rate S I
  21. 21. SIR ModelSusceptible Infected λ: infection rate μ: recovery rate S I
  22. 22. SIR Model Recovered/Susceptible Infected λ: infection rate μ: recovery rate Death S I R
  23. 23. SIR Model Recovered/Susceptible Infected λ: infection rate μ: recovery rate Death S I R dS =-λSI dt Epidemic size dI =λSI-μI = final #R / n dt
  24. 24. Network InterventionIntervention strategies1 : Reassigning patients to medical doctors[担当入院患者を診療科内で入れ替え]2 : Dissolving doctors’ teams [医師のチーム解消]3 : Introduction of single rooms [病室の個室化]
  25. 25. Network InterventionIntervention strategies cost1 : Reassigning patients to medical doctors[担当入院患者を診療科内で入れ替え]2 : Dissolving doctors’ teams [医師のチーム解消]3 : Introduction of single rooms [病室の個室化]
  26. 26. Network Intervention
  27. 27. Network InterventionDr t1 t2 w w Ns Ns
  28. 28. Network InterventionDr t1 t2 w w Ns Ns
  29. 29. Network InterventionDr t1 t2 w w Ns Ns intervention 1 [患者入れ替え]
  30. 30. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 [患者入れ替え]
  31. 31. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 [患者入れ替え]
  32. 32. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  33. 33. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  34. 34. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 [患者入れ替え] [チーム解消]
  35. 35. Network InterventionDr t1 t2 Dr Dr w w Ns Ns Pt1 intervention 1 intervention 2 intervention 3 [患者入れ替え] [チーム解消] [病室個室化]
  36. 36. Network Intervention better Effectiveness: 2 > 1 > 3
  37. 37. vaccination• Constraint: only 20 out of 605 can be vaccinated beforehand (by assuming R state in the SIR dynamics at time 0).• Strategies for selecting vaccinated individuals • Random [無作為] • Degree based [次数] • Betweenness centrality based [媒介中心性] • High-betweenness individuals are mostly Drs. Some Ns also included. • Recalculated betweenness centrality based
  38. 38. better
  39. 39. Conclusions• The observed hospital social network is small-world and hierarchical.• Healthcare workers (particularly Dr) are main transmitters. [医師が感染を媒介する]• Intervention: Restricting interaction between Drs and their visits to different wards is more effective than isolating Pts in single rooms. [チーム解消, 患者入れ替え > 病室個室化]• Vaccination: betweenness-based > degree-based. [媒介中心 性でワクチン接種するとよい] • Mostly doctors, some nurses • But the effectiveness of betweenness-based vaccination may not be general for networks with hierarchical or community structure.

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