ArticleEpidemiology (Cambridge, Mass.)2020
Tracing the Origin of Food-borne Disease Outbreaks: A Network Model Approach.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
- Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.Veterinary research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.