Published December 3, 2025
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Pre-exposure vaccination in the high-risk population is crucial in controlling mpox resurgence in Canada.

  • 1. Disease-Informed Modelling, Methods, and Systems (DIMMS) Lab, Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • 2. Department of Mathematics, Federal University of Technology, Owerri, Nigeria.
  • 3. Biostatistics, Bioinformatics, and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
  • 4. Fred Hutchinson Cancer Research Center
  • 5. Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada.
  • 6. Artificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, ON, M5T 3M7, Canada.
  • 7. University of Toronto
  • 8. Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Toronto, ON, Canada.
  • 9. Department of Mathematics & Statistics, Trent University Peterborough, Peterborough, ON, Canada.
  • 10. Department of Clinical Pharmacy, Saarland University, 66123, Saarbrücken, Germany.
  • 11. Saarland University
  • 12. Department of Food and Drugs, University of Parma, 43125, Parma, Italy.
  • 13. United Nations Educational, Scientific and Cultural Organization (UNESCO), Health Anthropology Biosphere and Healing Systems, University of Genoa, 16126, Genoa, Italy.
  • 14. University of Genoa
  • 15. Department of Computer Science, Data Science, and Information Technology, Faculty of Natural and Applied Sciences, Sol Plaatje University, Kimberley, South Africa.
  • 16. Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC), Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada. jude.kong@utoronto.ca.
  • 17. Artificial Intelligence & Mathematical Modeling Lab (AIMM Lab), Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, ON, M5T 3M7, Canada. jude.kong@utoronto.ca.
  • 18. Department of Mathematics, University of Toronto, Ontario, Canada. jude.kong@utoronto.ca.
  • 19. Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Toronto, ON, Canada. jude.kong@utoronto.ca.
  • 20. Disease-Informed Modelling, Methods, and Systems (DIMMS) Lab, Department of Mathematics and Statistics, York University, Toronto, ON, Canada. wassefaw@yorku.ca.
  • 21. Department of Mathematics, CNCS, Mekelle University, Tigray, Ethiopia. wassefaw@yorku.ca.

Description

As mpox spread continues across several endemic and non-endemic countries around the world, vaccination has become an integral part of the global response to control the epidemic. Some vaccines have been recommended for use against mpox by the World Health Organization (WHO). As the roll-out of mpox vaccines continue across the globe, it is imperative to develop mathematical models to support public health officials and governments agencies in optimizing vaccination strategies to curtail the resurgence of mpox. In this article, we develop a compartmental mathematical model to investigate the impact of vaccination in controlling a potential mpox resurgence in Canada. The model categorizes individuals into high- and low-risk groups and incorporates pre-exposure vaccination in the high-risk group and post-exposure vaccination in the high- and low-risk groups. The vaccine-free version of the model was calibrated to the daily reported cases of mpox in Canada from April to October 2022, from which we estimated key model parameters, including the sexual and non-sexual transmission rates. Furthermore, we calibrated the full model to the daily reported cases of mpox in Canada in 2024, to estimate the current mpox vaccination rates in Canada. Our results highlight the importance of pre-exposure vaccination in the high-risk group on controlling a potential resurgence of mpox in Canada, and the minimal effects of post-exposure vaccination in the high- and low-risk groups on the outbreak. In addition, our model predicts the possibility of mpox becoming endemic in Canada, in the absence of pre-exposure vaccination in the high-risk group. Overall, our modeling result suggests that pre-exposure vaccination in the high-risk group is crucial in controlling mpox outbreak in Canada. Stepping up this vaccination is sufficient to avert a potential mpox resurgence in Canada.Clinical trial number Not applicable.
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