Biomedical subjects

Caroline Colijn

Publications and source records attributed to Caroline Colijn.

5 recordsLinked to original sources

PubMed2026

A reusable model of pangenome selection informs optimal surveillance strategies over vaccine introductions.

BACKGROUND: The human pathogen Streptococcus pneumoniae is a major cause of disease, including pneumonia and meningitis. The introduction of Pneumococcal Conjugate Vaccines (PCVs) initially reduced the burden of disease through a reduction of colonisation by vaccine-targeted serotypes. However, since PCVs only target a proportion of pneumococcal serotypes, they shift intraspecific competition, eventually allowing non-targeted types to 'replace' vaccine types. Understanding the host and pathogen factors causing replacement is important for future vaccine development. Mechanistic understanding of vaccine replacement dynamics is crucial for forecasting and optimisation of genomic surveillance strategies to evaluate realised vaccine effectiveness. METHODS: We developed a mathematical model of the genomic and demographic factors which explain vaccine replacement, used this model to replicate serotype-frequency changes, and investigated cost-effective genomic surveillance strategies. We extended a forward-time model based on the Wright-Fisher model, developing a user-friendly model framework that describes the post-vaccine dynamics of S. pneumoniae populations. Our model describes vaccine replacement as a function of vaccine impact, immigration of new strains, and negative frequency-dependent selection (NFDS) on the accessory genome content. RESULTS: We used our model to study vaccine replacement in newly sequenced genomic surveillance data from Kathmandu (Nepal), and existing data from Massachusetts (US) and Southampton (UK), with distinct surveillance strategies. We showed that the model with NFDS better replicates replacement dynamics than a null model without NFDS, and that NFDS likely only acts on part of the S. pneumoniae accessory genome. We found consistent estimates for vaccination effectiveness across the different study locations and region-specific genes under NFDS, highlighting the importance of conducting genomic surveillance in each country of interest. By simulating data from the model, we showed that an optimal surveillance strategy prioritises per-sampling sample size over sampling frequency for small sampling budgets. CONCLUSIONS: Our model can be used to predict vaccine replacement dynamics after PCV introduction, and can be easily reapplied to analyse new data from vaccine introductions or new regions. Our model is available in the R package Stubentiger (Studying Balancing Evolution (NFDS) To Investigate Genome Replacement) on GitHub https://github.com/bacpop/Stubentiger .

Streptococcus pneumoniae↗

PubMed2026

Lineage-specific transmission and spatial clustering of Mycobacterium tuberculosis in Kaohsiung, Taiwan, in 2019-23: a population-based genomic study.

BACKGROUND: The epidemiology of tuberculosis in Taiwan has been influenced by the introduction of multiple Mycobacterium tuberculosis lineages and by the ageing of the population. We conducted a population-based study to investigate M tuberculosis transmission in Kaohsiung, a city in southern Taiwan. METHODS: In this study, we performed whole-genome sequencing (WGS) of M tuberculosis isolates from all culture-positive cases of tuberculosis notified in Kaohsiung between Jan 1, 2019 and Dec 31, 2023. We obtained routine epidemiological data for each case collected through the national tuberculosis control programme. We characterised the lineage composition of the isolate collection and evaluated genomic clustering of isolates, defined as a difference of 12 or fewer single-nucleotide polymorphisms. Univariable and multivariable logistic regression analyses were performed to estimate the odds of a case belonging to a genomic cluster based on host factors (age, sex, sputum smear status, and residential region) and pathogen factors (drug resistance status and strain lineage). Spatial aggregation of large genomic clusters (including greater than or equal to ten isolates) was assessed using a non-parametric statistical clustering method. We used a Bayesian transmission tree inference method to explore the patterns of age-dependent transmission. FINDINGS: During the study period, 5667 tuberculosis cases were notified in Kaohsiung, 4916 (86·7%) of which were culture-positive. Of these 4916 cases, whole-genome sequencing was successfully performed for 4168 (84·8%) isolates. 1219 (29·2%) of 4168 individuals were female and 2947 (70·7%) were male; the median age was 69·7 years (IQR 57·4-80·7). The dominant lineages were lineage 1 (1749 [42·0%] of 4168 isolates), lineage 2 (1510 [36·2%]), and lineage 4 (905 [21·7%]). 1069 (25·6%) of 4168 were genomically linked and formed 287 clusters. Lineage 2 isolates had higher odds (aOR 2·15 [95% CI 1·80-2·52]) than lineage 1 isolates of genomic clustering across all regions, whereas lineage 4 isolates had a significantly higher risk (2·75 [1·16-6·89]) of genomic clustering than lineage 1 only in the rural northeast region, inhabited primarily by indigenous populations. Spatial clustering analysis corroborated these lineage-region interactions. Although younger adults (<35 years) had the highest individual-level odds (5·64 [4·16-7·68]) of clustering in the logistic regression analysis compared with those aged 80 years or older, the transmission inference indicated that individuals aged 55-74 years were responsible for a greater proportion of inferred transmission events, contributing 50·8% of all transmission events. INTERPRETATION: This sequencing study revealed that older adults (aged ≥65 years) might have played a substantial and under-recognised role in the transmission of tuberculosis in Taiwan. The lineage-specific clustering and spatial patterns suggested that both pathogen characteristics and host demographics shaped tuberculosis transmission dynamics. These findings support the use of integrated genomic surveillance to guide precision tuberculosis control and motivate further research on age-specific transmission pathways and targeted interventions to advance tuberculosis elimination efforts. FUNDING: Taiwan National Health Research Institutes and Taiwan National Science and Technology Council.

Mycobacterium tuberculosis↗

PubMed2026

Comparison of phylogenetic metrics of transmission between symptomatic and asymptomatic tuberculosis in individuals who were incarcerated in Brazil in 2008-24: a retrospective genomic epidemiology study.

BACKGROUND: Tuberculosis control efforts have traditionally targeted symptomatic individuals; however, the role of asymptomatic cases in sustaining transmission is increasingly recognised. We aimed to quantify the contribution of asymptomatic tuberculosis to recent transmission using genomic and epidemiological data from a high-transmission setting. METHODS: We conducted a retrospective genomic epidemiology study of Mycobacterium tuberculosis isolates collected in Mato Grosso do Sul, Brazil, between Aug 25, 2008, and March 19, 2024. Available isolates underwent whole-genome sequencing. Demographic, clinical, incarceration history, and laboratory metadata were obtained from surveillance records. From Jan 1, 2017, to March 19, 2024, active case finding was conducted in the state's three largest prisons (all male-only facilities), during which sputum samples were collected from individuals irrespective of symptoms and tested using GeneXpert and culture. Comparisons of transmission between individuals with and without symptoms were restricted to individuals who were incarcerated and were identified through active case finding and for whom high-quality, M tuberculosis lineage 4 genomes were available. Metrics of recent transmission included phylogenetic clustering, time-scaled haplotype density (THD), local branching index (LBI), and transmission probabilities inferred using Bayesian Reconstruction and Evolutionary Analysis of Transmission Histories. FINDINGS: 4448 tuberculosis cases were notified in Mato Grosso do Sul in 2008-24. After excluding cases for which M tuberculosis isolates were not available or had low sequencing quality, who had contaminated cultures or mixed infection, or who were infected with non-lineage 4 M tuberculosis, we included 2362 lineage 4 M tuberculosis isolates with high-quality genome sequences. 1849 (78·3%) of 2362 isolates were part of a genomic cluster. Among 2362 individuals with tuberculosis, 1137 (48·1%) were incarcerated at diagnosis. Of these individuals, 505 were identified through active case finding in three male-only prisons. The median age was 30 years (IQR 25-37); 304 (60·2%) had mixed ethnicity, 90 (17·8%) were White, 56 (11·1%) were Black, 13 (2·6%) were Indigenous, and six (1·2%) were Asian. 277 (54·9%) had symptomatic disease and 228 (45·1%) had asymptomatic tuberculosis. There were no significant differences between symptomatic and asymptomatic individuals in phylogenetic clustering (213 [76·9%] of 277 vs 195 [85·5%] of 228; p=0·37), THD (median 0·39 [IQR 0·06-0·62] vs 0·50 [0·09-0·65]; p=0·12), or LBI (0·00863 [0·00810-0·00988] vs 0·00871 [0·00829-0·01020]; p=0·088). Bayesian transmission trees showed no significant difference in the number of secondary infections inferred from symptomatic compared with asymptomatic individuals (p=0·56). These findings were consistent across genomic clusters and robust to model assumptions. INTERPRETATION: We identified no differences in transmission between individuals who were symptomatic and those who were asymptomatic using multiple genomic measures. In this high-transmission setting, where systematic screening is implemented, our findings indicate that asymptomatic tuberculosis substantially contributes to tuberculosis transmission at the population level. These results suggest that symptom-based case detection alone is likely to be insufficient to interrupt transmission and highlight the importance of expanded screening strategies in high-risk populations. FUNDING: US National Institutes of Health and the Brazilian National Research Council (CNPq).

Humans↗

PubMed2025

Comparison of phylogenetic metrics of transmission in symptomatic and asymptomatic tuberculosis.

BACKGROUND: Understanding drivers of Mycobacterium tuberculosis (Mtb) transmission remains a critical challenge in high-burden settings. Tuberculosis control efforts traditionally target symptomatic individuals, yet the role of asymptomatic cases in sustaining transmission is increasing recognized. METHODS: We conducted a genomic and epidemiological analysis of Mtb isolates collected in Mato Grosso do Sul, Brazil, between 2008 and 2024. From 2017 to 2022, active case finding was performed in three of the state's largest prisons, whereby sputum was collected from individuals irrespective of symptoms and tested by GeneXpert and culture. We evaluated several metrics of recent transmission from symptomatic and asymptomatic individuals, including phylogenetic clustering, Time-scaled Haplotype Density (THD), Local Branching Index (LBI), and transmission probabilities inferred using the Bayesian Reconstruction and Evolutionary Analysis of Transmission Histories (BREATH). FINDINGS: We sequenced 2,362 Mtb strains, of which 3.5% (115/2,362) were resistant to at least one drug, and 0.6% (16/2,362) were multi-drug resistant. Most strains were lineage 4, and 78.2% of all isolates were part of a genomic cluster. Among 2,362 individuals with tuberculosis, 1,137 were incarcerated at the time of diagnosis. Among these, 505 were identified through active case finding: 277 had symptomatic disease and 228 had asymptomatic tuberculosis. There was no significant difference in phylogenetic clustering proportion (77% vs. 85%; p= 0.816), THD (median 0.50 vs. 0.39; p = 0.120), or LBI (median 0.00863 vs. 0.00871; p = 0.086) between symptomatic and asymptomatic individuals. Bayesian transmission trees revealed no significant difference in the number of secondary infections inferred from symptomatic compared with asymptomatic individuals (p = 0.56). These findings were consistent across genomic clusters and robust to model assumptions. INTERPRETATION: We identified no differences in transmission from symptomatic compared with asymptomatic individuals, using several genomic measures of transmission, underscoring the substantial contribution that asymptomatic tuberculosis makes to transmission at the population level.

Asymptomatic↗

PubMed2025

Integrating genomic and spatial analyses to describe tuberculosis transmission: a scoping review.

Tuberculosis remains a leading cause of infection-related mortality, and efforts to reduce its incidence have been hindered by an incomplete understanding of local Mycobacterium tuberculosis transmission dynamics. Advances in pathogen sequencing and spatial analysis have created new opportunities to map M tuberculosis transmission patterns more precisely. In this scoping review, we searched for studies combining pathogen genetics and location data to analyse the spatial patterns of M tuberculosis transmission and identified 142 studies published between 1994 and 2024. Secular changes in genetic methods were observed, with genome sequencing approaches largely replacing lower-resolution genotyping methods since 2020. The included studies addressed four primary research questions: how are tuberculosis cases and M tuberculosis transmission clusters geographically distributed; do spatially concentrated M tuberculosis clusters exist, and where are these areas located; when spatial concentration occurs, what host, pathogen, or environmental factors contribute to these patterns; and do identifiable relationships exist between the spatial proximity of tuberculosis cases and the genetic similarity of the M tuberculosis isolates infecting these individuals? Collectively, in this Review, we examined the available study data, evaluated the analytical requirements for addressing these questions, and discussed opportunities and challenges for future research. We found that the integration of spatial and genomic data can inform a detailed understanding of local M tuberculosis transmission patterns, but improved study designs and new analytical methods to address gaps in sampling completeness and to integrate additional movement data are needed to fully realise the potential of these tools.

Humans↗