ArticleScientific reports2016
Using heterogeneity in the population structure of U.S. swine farms to compare transmission models for porcine epidemic diarrhoea.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Current Evolutionary Dynamics of Porcine Epidemic Diarrhea Virus (PEDV) in the U.S. a Decade After Introduction.Viruses · 2025Article
- Postepidemic Epidemiology of Porcine Epidemic Diarrhea Virus in the United States.Transboundary and emerging diseases · 2024Article
- Evaluating the Risk Factors forFrontiers in veterinary science · 2020Article
- Spatio-temporal patterns and characteristics of swine shipments in the U.S. based on Interstate Certificates of Veterinary Inspection.Scientific reports · 2019Article
- Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods.Scientific reports · 2019Article
- Risk Attitudes Affect Livestock Biosecurity Decisions With Ramifications for Disease Control in a Simulated Production System.Frontiers in veterinary science · 2019Article
- Stability of Porcine Epidemic Diarrhea Virus on Fomite Materials at Different Temperatures.Veterinary sciences · 2018Article
- Evaluation of biosecurity measures to prevent indirect transmission of porcine epidemic diarrhea virus.BMC veterinary research · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In 2013, U.S. swine producers were confronted with the disruptive emergence of porcine epidemic diarrhoea (PED). Movement of animals among farms is hypothesised to have played a role in the spread of PED among farms. Via this or other mechanisms, the rate of spread may also depend on the geographic density of farms and climate. To evaluate such effects on a large scale, we analyse state-level counts of outbreaks with variables describing the distribution of farm sizes and types, aggregate flows of animals among farms, and an index of climate. Our first main finding is that it is possible for a correlation analysis to be sensitive to transmission model parameters. This finding is based on a global sensitivity analysis of correlations on simulated data that included a biased and noisy observation model based on the available PED data. Our second main finding is that flows are significantly associated with the reports of PED outbreaks. This finding is based on correlations of pairwise relationships and regression modeling of total and weekly outbreak counts. These findings illustrate how variation in population structure may be employed along with observational data to improve understanding of disease spread.
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