Researchers from North Carolina State University have developed an algorithm that could give pig farms advance notice of porcine epidemic diarrhea virus (PEDV) outbreaks. The proof-of-concept algorithm has potential for use in real-time prediction of other disease outbreaks in food animals.
PEDV is a virus that causes high mortality rates in preweaned piglets. The virus emerged in the U.S. in 2013 and by 2014 had infected approximately 50 percent of breeding herds. PEDV is transmitted by contact with contaminated fecal matter.
Gustavo Machado, assistant professor of population health and pathobiology at NC State and corresponding author of a paper describing the work, developed a pipeline utilizing machine-learning techniques to create an algorithm capable of predicting PEDV outbreaks in space and time.
Machado, with colleagues from the University of Minnesota and Brazil’s Universidade Federal do Rio Grande do Sul, used weekly farm-level incidence data from sow farms to create the model. The data included all pig movement types, hog density, and environmental and weather factors such as vegetation, wind speed, temperature and precipitation.
The researchers looked at “neighborhoods” that were defined as a 10-kilometer radius around sow farms. They fed the model information about outbreaks, animal movements into each neighborhood and the environmental characteristics inside each neighborhood. Ultimately, their model was able to predict PEDV outbreaks with approximately 80 percent accuracy.
The most important risk factor for predicting PEDV spread was pig movement into and through the 10 km neighborhood, although neighborhood environment – including slope and vegetation – also influenced risk.
“This proof-of-concept model identified the PEDV spread bottleneck in North Carolina and allowed us to rank infection risk factors in order of importance,” Machado says. “As we get more data from other farm sites across the U.S., we expect the model’s accuracy to increase. Our end goal is to have near real-time risk predictions so that farmers and veterinarians can provide preventative care to high-risk areas and make decisions based on data.”
Next steps for the researchers include improving the model to predict a wider range of diseases and expanding it to include other industries, such as poultry.
The Latest on: Machine learning predictions
via Google News
The Latest on: Machine learning predictions
- Machine-learning competition boosts earthquake prediction capabilitieson July 18, 2019 at 10:05 am
LOS ALAMOS, N.M., July 18, 2019—Three teams who applied novel machine learning methods to successfully predict the timing of earthquakes from historic seismic data are splitting $50,000 in prize money ... […]
- LANL: Machine-learning Competition Boosts Earthquake Prediction Capabilitieson July 18, 2019 at 9:29 am
Copyright © 2012-2019 Los Alamos Daily Post - the Official Newspaper of Record in Los Alamos County. This Site and all information contained here including, but not limited to, news stories, ... […]
- Applying devops in data science and machine learningon July 18, 2019 at 3:06 am
For software developers this often means custom coding applications and microservices; data scientists implement data integrations with dataops, make predictions through analytical models, and create ... […]
- Application of machine learning methods to healthcare outcomes researchon July 16, 2019 at 6:10 am
Machine learning methods may be useful to health service researchers seeking to improve prediction of a healthcare outcome with large datasets available to train and refine an estimator algorithm. ... […]
- What Are The Differences Between Econometrics, Statistics, And Machine Learning?on July 12, 2019 at 3:03 pm
and machine learning answer different sorts of questions. ML excels at finding patterns in data and using these patterns for classification and prediction. I discovered this myself a couple years ago, ... […]
- Machine learning predicts individual malaria outcomes, disease progressionon July 11, 2019 at 11:21 am
Machine learning analysis of patient data was able to predict the ... The novel method represents an innovative and viable approach for determining disease pathways by forming predictions based on ... […]
- Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRIon July 11, 2019 at 2:14 am
We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density ... […]
- Machine Learning Tools Help Predict Clinical Trial Outcomeson July 8, 2019 at 10:03 am
In addition to machine learning techniques, the team used statistical methods to account for missing data. These methods made it possible to estimate missing values along with other algorithm features ... […]
- Kaiser Permanente develops machine learning tool to predict HIV riskon July 8, 2019 at 9:03 am
This group of flagged patients included nearly half the men who later became infected, a significant improvement from other published HIV risk prediction tools, the study said. RELATED: Google, Verily ... […]
- Machine learning models at the touch of a fingeron July 5, 2019 at 11:15 am
To make it easier run complex analytics, researchers at MIT have developed a "virtual data scientist" that leverages user-friendly interfaces to generate machine-learning models for making predictions ... […]
via Bing News