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Measuring distance through dense weighted networks: The case of hospital-associated pathogens.

Abstract Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014-2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.
PMID
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Authors

Mayor MeshTerms
Keywords
Journal Title plos computational biology
Publication Year Start




PMID- 28771581
OWN - NLM
STAT- MEDLINE
DA  - 20170803
DCOM- 20170822
LR  - 20170822
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Linking)
VI  - 13
IP  - 8
DP  - 2017 Aug
TI  - Measuring distance through dense weighted networks: The case of
      hospital-associated pathogens.
PG  - e1005622
LID - 10.1371/journal.pcbi.1005622 [doi]
AB  - Hospital networks, formed by patients visiting multiple hospitals, affect the
      spread of hospital-associated infections, resulting in differences in risks for
      hospitals depending on their network position. These networks are increasingly
      used to inform strategies to prevent and control the spread of
      hospital-associated pathogens. However, many studies only consider patients that 
      are received directly from the initial hospital, without considering the effect
      of indirect trajectories through the network. We determine the optimal way to
      measure the distance between hospitals within the network, by reconstructing the 
      English hospital network based on shared patients in 2014-2015, and simulating
      the spread of a hospital-associated pathogen between hospitals, taking into
      consideration that each intermediate hospital conveys a delay in the further
      spread of the pathogen. While the risk of transferring a hospital-associated
      pathogen between directly neighbouring hospitals is a direct reflection of the
      number of shared patients, the distance between two hospitals far-away in the
      network is determined largely by the number of intermediate hospitals in the
      network. Because the network is dense, most long distance transmission chains in 
      fact involve only few intermediate steps, spreading along the many weak links.
      The dense connectivity of hospital networks, together with a strong regional
      structure, causes hospital-associated pathogens to spread from the initial
      outbreak in a two-step process: first, the directly surrounding hospitals are
      affected through the strong connections, second all other hospitals receive
      introductions through the multitude of weaker links. Although the strong
      connections matter for local spread, weak links in the network can offer ideal
      routes for hospital-associated pathogens to travel further faster. This hold
      important implications for infection prevention and control efforts: if a local
      outbreak is not controlled in time, colonised patients will appear in other
      regions, irrespective of the distance to the initial outbreak, making import
      screening ever more difficult.
FAU - Donker, Tjibbe
AU  - Donker T
AUID- ORCID: http://orcid.org/0000-0001-9022-4240
AD  - The National Institute for Health Research (NIHR) Health Protection Research Unit
      in Healthcare Associated Infections and Antimicrobial Resistance at University of
      Oxford in partnership with Public Health England, Oxford, United Kingdom.
AD  - Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
AD  - National Infection Service, Public Health England, Colindale, London, United
      Kingdom.
FAU - Smieszek, Timo
AU  - Smieszek T
AD  - National Infection Service, Public Health England, Colindale, London, United
      Kingdom.
AD  - MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease 
      Epidemiology, School of Public Health, Imperial College London, London, United
      Kingdom.
FAU - Henderson, Katherine L
AU  - Henderson KL
AD  - National Infection Service, Public Health England, Colindale, London, United
      Kingdom.
FAU - Johnson, Alan P
AU  - Johnson AP
AD  - The National Institute for Health Research (NIHR) Health Protection Research Unit
      in Healthcare Associated Infections and Antimicrobial Resistance at University of
      Oxford in partnership with Public Health England, Oxford, United Kingdom.
AD  - National Infection Service, Public Health England, Colindale, London, United
      Kingdom.
FAU - Walker, A Sarah
AU  - Walker AS
AUID- ORCID: http://orcid.org/0000-0002-0412-8509
AD  - The National Institute for Health Research (NIHR) Health Protection Research Unit
      in Healthcare Associated Infections and Antimicrobial Resistance at University of
      Oxford in partnership with Public Health England, Oxford, United Kingdom.
AD  - Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
AD  - NIHR Biomedical Research Centre, Oxford, Oxford, United Kingdom.
FAU - Robotham, Julie V
AU  - Robotham JV
AD  - The National Institute for Health Research (NIHR) Health Protection Research Unit
      in Healthcare Associated Infections and Antimicrobial Resistance at University of
      Oxford in partnership with Public Health England, Oxford, United Kingdom.
AD  - National Infection Service, Public Health England, Colindale, London, United
      Kingdom.
LA  - eng
PT  - Journal Article
DEP - 20170803
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
SB  - IM
MH  - Computational Biology/*methods
MH  - Computer Simulation
MH  - Contact Tracing
MH  - Cross Infection/*epidemiology/prevention & control/*transmission
MH  - Disease Outbreaks/prevention & control/*statistics & numerical data
MH  - England/epidemiology
MH  - Hospitals/*supply & distribution
MH  - Humans
PMC - PMC5542422
EDAT- 2017/08/05 06:00
MHDA- 2017/08/23 06:00
CRDT- 2017/08/04 06:00
PHST- 2017/01/09 [received]
PHST- 2017/06/13 [accepted]
AID - 10.1371/journal.pcbi.1005622 [doi]
AID - PCOMPBIOL-D-17-00042 [pii]
PST - epublish
SO  - PLoS Comput Biol. 2017 Aug 3;13(8):e1005622. doi: 10.1371/journal.pcbi.1005622.
      eCollection 2017 Aug.