ArticleScientific reports2019
Identifying outbreaks of Porcine Epidemic Diarrhea virus through animal movements and spatial neighborhoods.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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
31 citing papers in PubMed.
- XGBoost-based early warning framework for farm-level PED risk using meteorological and herd management variables.iScience · 2026Article
- Porcine epidemic diarrhea virus: a model for coronavirus persistence and immune escape in intensive livestock systems.Porcine health management · 2026Review
- Risk of exotic disease introduction and propagation in the Austrian swine trade network.iScience · 2026Article
- Swine Enteric Coronaviruses: An Updated Overview of Epidemiology, Diagnosis, Prevention, and Control.Animals : an open access journal from MDPI · 2026Review
- Simulation design to find the welfare impacts of livestock trading and disease transmission.PloS one · 2024Article
- Postepidemic Epidemiology of Porcine Epidemic Diarrhea Virus in the United States.Transboundary and emerging diseases · 2024Article
- Infection prediction in swine populations with machine learning.Scientific reports · 2023Article
- The spectral underpinnings of pathogen spread on animal networks.Proceedings. Biological sciences · 2023Article
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- Disentangling transport movement patterns of trucks either transporting pigs or while empty within a swine production system before and during the COVID-19 epidemic.Frontiers in veterinary science · 2023Article
- A discrete-time survival model for porcine epidemic diarrhoea virus.Transboundary and emerging diseases · 2022Article
- Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance.Transboundary and emerging diseases · 2022Article
- Spatiotemporal analysis of reported classical swine fever outbreaks in China (2005-2018) and the influence of weather.Transboundary and emerging diseases · 2022Article
- Machine learning based personalized promotion strategy of piglets weaned per sow per year in large-scale pig farms.Porcine health management · 2022Article
- Asymmetric host movement reshapes local disease dynamics in metapopulations.Scientific reports · 2022Article
- The Application of an Augmented Gravity Model to Measure the Effects of a Regionalization of Potential Risk Distribution of the US Cull Sow Market.Veterinary sciences · 2022Article
- Article
- Article
- Oral Administration of Coronavirus Spike Protein Provides Protection to Newborn Pigs When Challenged with PEDV.Vaccines · 2021Article
- Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India.Journal of biomedical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of pathogens in swine populations is in part determined by movements of animals between farms. However, understanding additional characteristics that predict disease outbreaks and uncovering landscape factors related to between-farm spread are crucial steps toward risk mitigation. This study integrates animal movements with environmental risk factors to identify the occurrence of porcine epidemic diarrhea virus (PEDV) outbreaks. Using weekly farm-level incidence data from 332 sow farms, we applied machine-learning algorithms to quantify associations between risk factors and PEDV outbreaks with the ultimate goal of training predictive models and to identify the most important factors associated with PEDV occurrence. Our best algorithm was able to correctly predict whether an outbreak occurred during one-week periods with >80% accuracy. The most important predictors included pig movements into neighboring farms. Other important neighborhood attributes included hog density, environmental and weather factors such as vegetation, wind speed, temperature, and precipitation, and topographical features such as slope. Our neighborhood-based approach allowed us to simultaneously capture disease risks associated with long-distance animal movement as well as local spatial dynamics. The model presented here forms the foundation for near real-time disease mapping and will advance disease surveillance and control for endemic swine pathogens in the United States.
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.