Current Projects
Near-real time spatiotemporal resource allocation to improve swine health
Funded by USDA-NIFA FACT.
Animal Movement Networks, Risk Based Targeted Surveillance, and Dynamics of Disease Transmission
Funded by FUNDESA-RS.
Estimate the impacts of movement restrictions under the national African swine fever response plan
Funded by USDA-APHIS NADPRP.
Past Projects
Dynamic Transmission Modeling: The Role of Feed, Feed Ingredients in Swine Disease Spread
Funded by Fats and Proteins Research Foundation.
The 2018 PEDv and PRRSv seasons were the less severe in the past six years. This could be related to many factors: virus genetics and evolution, improvement in farm biosecurity, the introduction of new PRRSv vaccines in the marketplace among other factors. Despite the unexplained reduction in the number of new cases last year, both viruses are still spreading throughout the country. Therefore, without improvements in the understanding of how these viruses spread between-farm, it is difficult to answer questions related to the main route of transmission, and best control interventions. The main transmission routes used by both viruses have been related to the movement of infected pigs (dead or alive), people and feed, and spillover from nearby infected farms. Thus, our objective is to assess the relative contribution of those main routes on the between-farm transmission of PEDv and PRRSv.
Publications:
Assessing biosecurity vulnerabilities to predict the risk of new Porcine Reproductive and Respiratory Syndrome outbreaks
Funded by USDA-NIFA CARE.
Our goal is to leverage a national research and extension program to develop an easy-to-use biosecurity tool, available to all producers, and economically effective, providing the industry with the capacity to greatly curtail PRRS virus introductions.
Publications:
- Silva, G.S., Machado,G.,Baker, K.L., Holtkamp, D.J., Linhares, D. Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak-2019
- Sykes,A.L., Silva,S.G., Holtkamp,J.D., Mauch,W.B., Osemeke,O., Linhares,C.L.D., Machado,G. Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus-2022