Geographical migration and fitness dynamics of Streptococcus pneumoniae: a perspective from contrasting selective pressures

Streptococcus pneumoniae remains a leading cause of pneumonia, meningitis, and sepsis worldwide, imposing a substantial burden on global public health (Cliff et al., 2024, Mahtab et al., 2024). The pathogen's remarkable genetic plasticity, encompassing over 100 serotypes and more than 900 lineages, has enabled it to adapt rapidly to clinical interventions, including antimicrobial therapy and vaccination (Ganaie et al., 2025, Hanage et al., 2009). While pneumococcal conjugate vaccines (PCVs) have dramatically reduced vaccine-type disease in many high-income countries, serotype replacement and the expansion of resistant non-vaccine types have emerged as new challenges (Garcia Quesada et al., 2025, Bennett et al., 2025). Simultaneously, antimicrobial resistance (AMR) continues to escalate, particularly for macrolides and β-lactams, threatening the effectiveness of empirical treatment regimens (Zhou et al., 2022, Bruinsma et al., 2004).Understanding the spatial spread and fitness dynamics of pneumococcal lineages is critical for designing effective surveillance and vaccination strategies. However, the complex interplay between human mobility, vaccine selection pressure, and antibiotic consumption remains poorly quantified, particularly in diverse epidemiological settings.Most phylogeographic studies have focused on high-income countries with established vaccination programs, leaving substantial gaps in our understanding of how these dynamics operate in regions with different selective pressures (Lo et al., 2019, Chaguza et al., 2016).In this context, Belman and colleagues recently published a landmark study in Nature that integrates genomic data from 6,910 South African isolates with Meta human mobility data to reconstruct pneumococcal geographical migration and vaccine-driven fitness dynamics (Belman et al., 2024). Their study makes three pivotal contributions.First, it quantitatively demonstrates that pneumococci require approximately 50 years of transmission to become homogeneously mixed across South Africa-a finding that challenges intuitive assumptions about bacterial spread and reveals the deeply "entrenched" nature of endemic transmission. Second, by coupling Meta mobility data with phylogeographic models, the authors establish a quantitative link between individual-level behaviour and pathogen macro-dispersal, showing that the "focal" nature of human movement drives slow spread. Third, they provide robust fitness estimates after PCV introduction: non-vaccine types (NVT) showed a relative fitness advantage of 1.68 (95% CI: 1.59-1.77) over vaccine types (VT), and penicillin resistance among NVT strains is gaining fitness, suggesting that vaccine-associated reductions in AMR may be transient (Belman et al., 2024).While we greatly appreciate this high-quality work, we do not question the validity of their findings within the South African context. Rather, we argue that the extent to which their quantitative conclusions can inform policy in settings with fundamentally different selective pressures-such as China, where PCV coverage is <5% but macrolide resistance exceeds 90%-remains an open empirical question. Our Chinese data do not invalidate Belman et al.'s findings but quantify how and to what extent pneumococcal fitness dynamics differ across contrasting epidemiological landscapes, thereby operationalizing the very context-dependence they themselves acknowledged (Belman et al., 2024, Limitations section).In this opinion article, drawing on our 14-year nationwide surveillance in China (2007-2020, n=1,312 isolates from 24 hospitals across 18 provinces) (Cui et al., 2026) and our phylogenomic analysis of the MDR clone CC271 (Zeng et al., 2023), we address a single overarching question: To what extent can phylogeographic and fitness estimates derived from one epidemiological setting inform surveillance and vaccine policy in another? We do not attempt to provide a definitive answer-such would require analyses across many more settings-but rather use the contrast between South Africa and China as an illustrative case study. Belman et al.'s study provides the reference point from a high-vaccine-coverage setting; our 14-year Chinese surveillance data provide the counterpoint from a low-vaccine-coverage, high-antibioticconsumption setting.The South African dataset, while comprehensive, reflects the epidemiological landscape of an upper-middle-income African country with high PCV coverage (89% by 2022). The serotype distribution, clonal composition, and AMR profiles observed in South Africa differ substantially from those in other regions (Belman et al., 2024). Our 14-year surveillance in China demonstrates that serotypes 19F and 19A dominate, ST271 and ST320 are the predominant clones, and >90% of isolates are macrolide-resistant-features that are not mirrored in the South African data (Cui et al., 2026). These differences are not merely descriptive but have profound implications for phylogeographic modelling. The "50-year" mixing time estimated for South Africa reflects the focal nature of human mobility in that specific geographic and demographic context. Countries with vastly different population densities, urban-rural distributions, transportation infrastructures, and mobility patterns may exhibit substantially different rates of pathogen dispersal. For instance, China's dense urban centres, extensive highspeed rail network, and high population mobility may facilitate more rapid interregional transmission than observed in South Africa. Whether the quantitative conclusions of Belman et al. can be extrapolated to countries with such differing characteristics remains an open question that requires validation through similar analyses in diverse settings.PCV introduction, which is plausible given the country's 89% coverage (Belman et al., 2024). However, in many parts of the world-especially low-and middle-income countries-pneumococcal vaccine uptake remains low (Clark et al., 2026). Our Chines