Of Lyme disease and machine learning in a One Health world.

Journal: American journal of veterinary research
PMID: 39933253

Abstract

OBJECTIVE: Lyme disease is a vector-borne emerging zoonosis in Ontario driven by human population growth and climate change. Lyme disease is also a prime example of the One Health concept. While little can be done to immediately reverse climate change and population growth, public health must resort to health communication as its best option for disease control until an effective vaccine becomes available. Disease surveillance enabling precision public health has an important role in this respect: one of the goals of disease surveillance is to forecast the future burden of disease to inform those who need to know. The goal of this study was to forecast the burden of Lyme disease using automated machine learning and statistical learning approaches.

Authors

  • Olaf Berke
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada; Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada; Centre for Advancing Responsible and Ethical Artificial Intelligence, University of Guelph, 50 Stone Road East, Guelph, Ontario, Canada. Electronic address: oberke@uoguelph.ca.
  • Sarah T Chan
    Centre for Data Management, Innovation and Analytics, Public Health Agency of Canada, Ottawa, ON, Canada.
  • Armin Orang
    Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada.

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