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Banuet-Martinez M, Yang Y, Jafari B, Kaur A, Butt ZA, Chen HH, Yanushkevich S, Moyles IR, Heffernan JM, Korosec CS. Monkeypox: a review of epidemiological modelling studies and how modelling has led to mechanistic insight. Epidemiol Infect 2023; 151:e121. [PMID: 37218612 PMCID: PMC10468816 DOI: 10.1017/s0950268823000791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/04/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023] Open
Abstract
Human monkeypox (mpox) virus is a viral zoonosis that belongs to the Orthopoxvirus genus of the Poxviridae family, which presents with similar symptoms as those seen in human smallpox patients. Mpox is an increasing concern globally, with over 80,000 cases in non-endemic countries as of December 2022. In this review, we provide a brief history and ecology of mpox, its basic virology, and the key differences in mpox viral fitness traits before and after 2022. We summarize and critique current knowledge from epidemiological mathematical models, within-host models, and between-host transmission models using the One Health approach, where we distinguish between models that focus on immunity from vaccination, geography, climatic variables, as well as animal models. We report various epidemiological parameters, such as the reproduction number, R0, in a condensed format to facilitate comparison between studies. We focus on how mathematical modelling studies have led to novel mechanistic insight into mpox transmission and pathogenesis. As mpox is predicted to lead to further infection peaks in many historically non-endemic countries, mathematical modelling studies of mpox can provide rapid actionable insights into viral dynamics to guide public health measures and mitigation strategies.
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Affiliation(s)
- Marina Banuet-Martinez
- Climate Change and Global Health Research Group, School of Public Health, University of Alberta, Edmonton, AB, Canada
| | - Yang Yang
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Behnaz Jafari
- Mathematics and Statistics Department, Faculty of Science, University of Calgary, Calgary, AB, Canada
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Avneet Kaur
- Irving K. Barber School of Arts and Sciences, Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia Okanagan, Kelowna, BC, Canada
| | - Zahid A. Butt
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Helen H. Chen
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Svetlana Yanushkevich
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Iain R. Moyles
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, Toronto, ON, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Jane M. Heffernan
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, Toronto, ON, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Chapin S. Korosec
- Modelling Infection and Immunity Lab, Mathematics and Statistics, York University, Toronto, ON, Canada
- Centre for Disease Modelling, Mathematics and Statistics, York University, Toronto, ON, Canada
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