Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.

Journal: Artificial intelligence in medicine
PMID: 39914162

Abstract

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely beneficial to public health emergency management. This work aims to design and deploy a framework for real-time surveillance, prediction, forecasting, and early warning of respiratory disease. To this end, we selected southern African countries and Canadian provinces, along with COVID-19 and influenza as our case studies.

Authors

  • Z Movahedi Nia
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; Laboratory for Industrial and Applied Mathematics (LIAM), York University, Toronto, Canada.
  • L Seyyed-Kalantari
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Department of Electrical Engineering and Computer Science, York University, Toronto, Canada.
  • M Goitom
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; School of Social Work, York University, Toronto, ON M3J 1P3, Canada.
  • B Mellado
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; School of Physics, Institute for Collider Particle Physics, University of the Witwatersrand, Johannesburg, South Africa.
  • A Ahmadi
    Advanced Disaster, Emergency and Rapid-response Simulation (ADERSIM), York University, Toronto, Ontario, Canada.
  • A Asgary
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; Advanced Disaster, Emergency and Rapid-response Simulation (ADERSIM), York University, Toronto, Ontario, Canada.
  • J Orbinski
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; Dahdaleh Institute for Global Health Research, York University, Toronto, Canada.
  • J Wu
    Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • J D Kong
    Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Canada; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Canada; Artificial Intelligence & Mathematical Modelling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, 155 College St, Toronto, ON M5T 3M7, Canada; Institute of Health Policy, Management and Evaluation (IHPME), University of Toronto, Canada; Department of Mathematics, University of Toronto, Bahen Centre for Information Technology, 40 St. George Street, Toronto, ON M5S 2E4, Canada. Electronic address: jude.kong@utoronto.ca.

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