Pathogen population structure can explain hospital outbreaks

Abstract

Hospitalized patients are at risk for increased length of stay, illness, or death due to hospital acquired infections. The majority of hospital transmission models describe dynamics on the level of the host rather than on the level of the pathogens themselves. Accordingly, epidemiologists often cannot complete transmission chains without direct evidence of either host-host contact or a large reservoir population. Here, we propose an ecology-based model to explain the transmission of pathogens in hospitals. The model is based upon metapopulation biology, which describes a group of interacting localized populations and island biogeography, which provides a basis for how pathogens may be moving between locales. Computational simulation trials are used to assess the applicability of the model. Results indicate that pathogens survive for extended periods without the need for large reservoirs by living in localized ephemeral populations while continuously transmitting pathogens to new seed populations. Computational simulations show small populations spending significant portions of time at sizes too small to be detected by most surveillance protocols and that the number and type of these ephemeral populations enable the overall pathogen population to be sustained. By modeling hospital pathogens as a metapopulation, many observations characteristic of hospital acquired infection outbreaks for which there has previously been no sufficient biological explanation, including how and why empirically successful interventions work, can now be accounted for using population dynamic hypotheses. Epidemiological links between temporally isolated outbreaks are explained via pathogen population dynamics and potential outbreak intervention targets are identified.

Metapopulation ecology links antibiotic resistance, consumption, and patient transfers in a network of hospital wards

“…The metapopulation concept, in which populations of organisms are spread across inter-connected patches with varying characteristics, is useful to describe bacterial pathogens in a hospital network containing wards with different sizes and levels of antibiotic pressure, connected by the transfers of infected patients. Models using the metapopulation framework, beyond their frequent use in wildlife and conservation biology ( Dolrenry et al, 2014 ; Heard et al, 2015 ; MacPherson and Bright, 2011 ), have recently provided theoretical grounds for pathogen persistence in the healthcare setting ( Spagnolo et al, 2018 ). So far, however, metapopulation models of hospital AMR have been applied to simulated rather than empirical data ( Spagnolo et al, 2018 ; Vilches et al, 2019 ).…”

“…Models using the metapopulation framework, beyond their frequent use in wildlife and conservation biology ( Dolrenry et al, 2014 ; Heard et al, 2015 ; MacPherson and Bright, 2011 ), have recently provided theoretical grounds for pathogen persistence in the healthcare setting ( Spagnolo et al, 2018 ). So far, however, metapopulation models of hospital AMR have been applied to simulated rather than empirical data ( Spagnolo et al, 2018 ; Vilches et al, 2019 ).…”

Metapopulation ecology links antibiotic resistance, consumption and patient transfers in a network of hospital wards

“…It was introduced by Levins 21 to explain the persistence of agricultural pests across a set of habitat patches and refined by Hanski to account for the size of patches, the connectivity between them, and habitat quality within them 22,23 . Metapopulation models, beyond their frequent use in wildlife and conservation biology [24][25][26] , have recently provided theoretical grounds for pathogen persistence in the healthcare setting 27 . So far, however, metapopulation models of hospital AMR have been applied on simulated rather than empirical data 27,28 .…”

“…Metapopulation models, beyond their frequent use in wildlife and conservation biology [24][25][26] , have recently provided theoretical grounds for pathogen persistence in the healthcare setting 27 . So far, however, metapopulation models of hospital AMR have been applied on simulated rather than empirical data 27,28 .…”

Tracking Staphylococcus aureus in the intensive care unit using whole-genome sequencing

“…The mechanism by which patients acquire infection, including the role of the air, is still not fully understood so more studies exploring pathogen transmission in healthcare environments are required. 1,2 Meticillin-susceptible and resistant Staphylococcus aureus (MSSA and MRSA) provide useful markers for transmission since they frequently colonise patients, visitors and staff, and contaminate the environment including air. Dynamic transmission of staphylococci between patients, air and hand-touch surfaces, from direct contact and/or hand carriage, facilitates acquisition of S. aureus by patients.…”