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Spread of Pathogens in the Patient Transfer Network of US Hospitals

  • Juan Fernández-GraciaEmail author
  • Jukka-Pekka Onnela
  • Michael L. Barnett
  • Víctor M. Eguíluz
  • Nicholas A. Christakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Antibiotic-resistant organisms, an increasing source of morbidity and mortality, have a natural reservoir in hospitals, and recent estimates suggest that almost 2 million people develop hospital-acquired infections each year in the US alone. We investigate the temporal network of transfers of Medicare patients across US hospitals over a 2-year period to learn about the possible role of hospital-to-hospital transfers of patients in the spread of infections. We analyze temporal, geographical, and topological properties of the transfer network and show that this network may serve as a substrate for the spread of infections. Finally, we study different strategies for the early detection of incipient epidemics on the temporal transfer network as a function of activation time of a subset of sensor hospitals. We find that using approximately 2% of hospitals as sensors, chosen based on their network in-degree, with an activation time of 7 days results in optimal performance for this early warning system, enabling the early detection of 80% of the C. difficile. cases with the hospitals in the sensor set activated for only a fraction of 40% of the time.

Keywords

Nosocomial Infection Medicare Patient Temporal Network Patient Transfer Network Neighbor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Laurie Meneades for the expert assistance required to build the dataset. JFG and JPO are joint first authors of this article.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan Fernández-Gracia
    • 1
    • 2
    Email author
  • Jukka-Pekka Onnela
    • 3
  • Michael L. Barnett
    • 3
  • Víctor M. Eguíluz
    • 2
  • Nicholas A. Christakis
    • 4
  1. 1.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  2. 2.Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB)PalmaSpain
  3. 3.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  4. 4.Yale Institute for Network ScienceYale UniversityNew HavenUSA

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