Evidence map›Paper›PMID 30046167›Full record

ArticleThe ISME journal2018

Pathogen population structure can explain hospital outbreaks.

Fabrizio Spagnolo, Pierre Cristofari, Nicholas P Tatonetti, Lev R Ginzburg, Daniel E Dykhuizen

Abstract read
In one paragraph

Article in The ISME journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fabrizio SpagnoloEcology, Evolution and Environmental Biology Department, Columbia University, New York, NY, 10027, USA. fs2599@columbia.edu.ORCID http://orcid.org/0000-0002-0519-0427
Pierre CristofariDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY, 10032, USA.
Nicholas P TatonettiDepartment of Biomedical Informatics, Columbia University Medical Center, New York, NY, 10032, USA.ORCID http://orcid.org/0000-0002-2700-2597
Lev R GinzburgApplied Biomathematics, Setauket, NY, 11733, USA.
Daniel E DykhuizenDepartment of Ecology and Evolution, Stony Brook University, Stony Brook, NY, 11794, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Disease OutbreaksModels, TheoreticalComputer SimulationCross InfectionHospitalsHumansMonte Carlo MethodPopulation Dynamics

Identifiers

PMID30046167
PMCPMC6246595

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.