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This study addresses a relevant research question by examining how the genomic diversity, antimicrobial resistance patterns, and transmission dynamics of Treponema pallidum in Africa differ from those observed globally. The authors use an observational genomic epidemiology approach, combining cross-sectional and retrospective data with whole genome sequencing, which is appropriate for capturing both spatial and temporal patterns of variation.

The findings indicate that T. pallidum in Africa has considerable genomic diversity, with most infections (83.8%) driven by locally circulating sublineages, suggesting strong geographic structuring. Additionally, macrolide resistance is notably lower than global estimates and appears to be primarily associated with imported strains. Overall, the results support the authors’ conclusions, as the data and interpretations are well aligned and follow a coherent analytical progression from genomic analysis to epidemiological inference. These findings contribute valuable new knowledge to a field that has historically been limited by underrepresentation of African genomic data.

Among the main strengths, the study contributes valuable genomic data from an underrepresented region, helping to fill an important gap in global surveillance. The integration of African and global datasets allows meaningful comparisons, while the use of whole genome sequencing and phylogenetic analysis provides high-resolution insights into transmission dynamics and antimicrobial resistance patterns. This methodological approach strengthens the validity of the findings and enhances their relevance for public health.

However, some limitations should be considered. The most important is the limited and uneven geographic sampling, with most data coming from a small number of countries, which may affect the representativeness of the findings. This is particularly relevant when interpreting conclusions about predominantly local transmission, as the observed patterns may partly reflect the geographic concentration of samples and the reliance on clinical populations rather than fully capturing transmission dynamics across the region. Additionally, the use of samples obtained from clinical settings, primarily individuals presenting with symptomatic disease, introduces potential selection bias and may exclude asymptomatic or underserved populations that could play a role in transmission. Similarly, the finding of lower macrolide resistance should be interpreted with caution, since resistance patterns are known to vary across regions and over time, often influenced by local antibiotic use practices.

In conclusion, this is a well-conducted study that provides important insights into the genomic epidemiology of T. pallidum in Africa. While certain limitations related to sampling and representativeness should be more explicitly considered when interpreting the findings, the conclusions are supported by the data and the study offers a meaningful contribution to the field.

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they did not use generative AI to come up with new ideas for their review.