| Autor: |
Bragazzi NL; Department of Food and Drugs, University of Parma , Parma, Italy., Iyaniwura SA; Theoretical Biology and Biophysics Group, Los Alamos National Laboratory , Los Alamos, NM, USA., Han Q; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) , Toronto, Ontario, Canada.; Department of Mathematics and Statistics, Laboratory for Industrial and Applied Mathematics (LIAM), York University , Toronto, Ontario, Canada.; Artificial Intelligence and Mathematical Modelling Lab (AIMMLAb), Dalla Lana School of Public Health, University of Toronto , Toronto, Ontario, Canada., Woldegerima WA; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) , Toronto, Ontario, Canada.; Department of Mathematics and Statistics, Laboratory for Industrial and Applied Mathematics (LIAM), York University , Toronto, Ontario, Canada., Kong JD; Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) , Toronto, Ontario, Canada.; Artificial Intelligence and Mathematical Modelling Lab (AIMMLAb), Dalla Lana School of Public Health, University of Toronto , Toronto, Ontario, Canada.; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), University of Toronto , Toronto, Ontario M3J 1P3, Canada. |
| Abstrakt: |
In 2022, there was a global resurgence of mpox, with different clinical-epidemiological features compared with previous outbreaks. Sexual contact was hypothesized as the primary transmission route, and the community of men having sex with men (MSM) was disproportionately affected. Because of the stigma associated with sexually transmitted infections, the real burden of mpox could be masked. We quantified the basic reproduction number ( R 0 ) and the underestimated fraction of mpox cases in 16 countries, from the onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-/low-risk populations) and two-route (sexual/non-sexual transmission) mathematical model. Machine learning (ML) was harnessed to identify underestimation determinants. Estimated R 0 ranged between 1.37 (Canada) and 3.68 (Germany). The underestimation rates for the high- and low-risk populations varied between 25-93% and 65-85%, respectively. The estimated total number of mpox cases, relative to the reported cases, is highest in Colombia (3.60) and lowest in Canada (1.08). In the ML analysis, two clusters of countries could be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and the importance of religion. Given the substantial mpox underestimation, surveillance should be enhanced, and country-specific campaigns against the stigmatization of MSM should be organized, leveraging community-based interventions. |
