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• W2952033066 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." @default.
• W2952033066 created "2019-06-27" @default.
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• W2952033066 date "2018-07-25" @default.
• W2952033066 modified "2023-10-05" @default.
• W2952033066 title "Pathogen population structure can explain hospital outbreaks" @default.
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• W2952033066 doi "https://doi.org/10.1038/s41396-018-0235-5" @default.
• W2952033066 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6246595" @default.
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• W2952033066 hasPublicationYear "2018" @default.
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