Our research focuses on developing mathematical modelling methods and tools with direct application to emerging and transboundary animal diseases. Ultimately, we are interested in providing an understanding of infectious disease transmission processes among livestock populations.

Our team develops and applies epidemiological tools to investigate the occurrence and spread of animal infectious diseases, especially transboundary disease, providing science-based support for decision making with regard to prevention and control. In the past few years, we have dedicated efforts to the development and application of machine-learning approaches, applied to animal and human health. We have also developed and used methods for modeling disease distributions. Our research team works on the development of mathematical models to simulate the spread of infectious diseases among livestock systems (swine and bovine) to evaluate the effectiveness of control actions.

  • On-farm biosecurity
  • Disease modelling
  • Improve disease control and interventions
  • The Machado Lab is led by Gustavo Machado, an assistant professor at the North Carolina State University, College of Veterinary Medicine at the Department of Population Health and Pathobiology, with an affiliation with the Center for Geospatial Analytics.

    Lab news and events

  • October 15 2022: Workshop for Brazilian veterinary services of three states
  • December 11 2021: 3 new grants funded by USDA-APHIS-NADPRP (link here)
  • November 7 2021: A new paper!, led by PhD student Sykes Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus (link here)
  • November 27 2021: A new paper!, led by MS. Ellington, Unraveling the Contact Network Patterns between Commercial Turkey Operation in North Carolina and the Distribution of Salmonella Species (link here)
  • October 21 2021: A new paper!, led by Dr. Machado, Modelling the role of mortality-based response triggers on the effectiveness of African swine fever control strategies (link here)