Evidence map›Paper›PMID 32079833›Full record

ArticleEpidemiology (Cambridge, Mass.)2020

Tracing the Origin of Food-borne Disease Outbreaks: A Network Model Approach.

Rianne Jacobs, Peter Teunis, Jan van de Kassteele

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

3 authors.

Rianne JacobsFrom the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, the Netherlands.
Peter TeunisHubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA.
Jan van de KassteeleDepartment of Statistics, Informatics and Modelling, National Institute for Public Health and the Environment, Bilthoven, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFood-borne disease outbreaks constitute a large health burden on society. One of the challenges when investigating such outbreaks is to trace the origin of the outbreak. In this study, we consider a network model to determine the spatial origin of the contaminated food product that caused the outbreak.

methodsThe network model we use replaces the classic geographic distance of a network by an effective distance so that two nodes connected by a long-range link may be more strongly connected than their geographic distance would suggest. Furthermore, the effective distance transforms complex spatial patterns into regular topological patterns, creating a means for easier identification of the origin of the spreading phenomenon. Because detailed information on food distribution is generally not available, the model uses the gravity model from economics: the flow of goods from one node to another increases with population size and decreases with the geographical distance between them.

resultsThis effective distance network approach has been shown to perform well in a large Escherichia coli O104:H4 outbreak in Germany in 2011. In this article, we apply the same method to various food-borne disease outbreaks in the Netherlands. We found the effective distance network approach to fail in certain scenarios.

conclusionsGreat care should be taken as to whether the underlying network model correctly captures the spreading mechanism of the outbreak in terms of spatial scale and single or multiple source outbreak.

Indexed as

Disease OutbreaksFoodborne DiseasesGermanyHumansModels, TheoreticalNetherlands

Identifiers

PMID32079833
PMCPMC7144751

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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.