Search Results for author:
Found 6 papers, 2 papers with code
Spatiotemporal relative risk distribution of porcine reproductive and respiratory syndrome virus in the southeastern United States
Given the farm-to-farm proximity in high pig production areas, local transmission is an important pathway in the spread of PRRSV; however, there is limited understanding of the role local transmission plays in the dissemination of PRRSV, specifically, the distance at which there is increased risk for transmission from infected to susceptible farms.
The role of vehicle movement in swine disease dissemination: novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties
A large number of between-farm contacts with a pathogen stability >0. 8 were present in the vehicle network even with 100% cleaning effectiveness.
Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: characterizing the dominant transmission routes
Here, we modeled nine modes of between-farm transmission pathways including farm-to-farm proximity (local transmission), contact network of pig farm movements between sites, four different contact networks of transportation vehicles (vehicles that transport pigs from farm-to-farm, pigs to markets, feed distribution and crew), the volume of animal by-products within feed diets (e. g. animal fat and meat and bone meal) to reproduce PEDV transmission dynamics.
Modelling African swine fever virus spread in pigs using time-respective network data: scientific support for decision-makers
Raw movement data were directly used to build a dynamic network on a realistic time-scale.
Multiple species animal movements: network properties, disease dynamic and the impact of targeted control actions
Our analysis showed that the swine network was more connected than cattle and small ruminants in the temporal network view.
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus
Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices, to produce a predicted outbreak risk.
