Defining new Buruli ulcer endemic areas in urban southeastern Australia using bacterial genomics-informed possum excreta surveys
Bibliographic record
Abstract
ABSTRACT Buruli ulcer in southeastern Australia is a zoonosis caused by infection with Mycobacterium ulcerans . Australian native possums are a major wildlife reservoir, and infected possums shed M. ulcerans in their excreta, with mosquitoes being the major transmitting vector in this region. Buruli ulcer is geographically restricted, and this feature, combined with an average 4.8-month incubation period, makes tracking M. ulcerans environmental spread and timely identification of new Buruli ulcer endemic areas challenging. While human mobility complicates transmission tracing, we used the highly territorial behavior of native possums and high-resolution pathogen genomics to confidently identify new Buruli ulcer endemic areas in Melbourne’s inner northwest and southern suburbs of Geelong. Using pathogen genomic phylodynamic modeling, we estimated that M. ulcerans was introduced to these areas 2–6 years before the emergence of human Buruli ulcer cases. This study shows how possum excreta surveys combined with pathogen genome data can pinpoint new Buruli ulcer endemic areas, thus providing critical local knowledge for targeted public health interventions to reduce exposure risk and ensure early diagnosis. IMPORTANCE Buruli ulcer is a debilitating skin disease caused by Mycobacterium ulcerans , with transmission in southeastern Australia involving native possums as wildlife reservoirs and mosquitoes as vectors. Early detection of new endemic areas is critical to preventing human disease, yet is hindered by the pathogen’s long incubation period, highly focal transmission, and the mobility of human hosts. This study demonstrates how structured possum excreta surveys, combined with high-resolution pathogen genomics, can confidently identify new Buruli ulcer endemic areas in southeastern Australia. By confirming local transmission in Melbourne’s inner northwest and Geelong through genome matching between possum- and human-derived M. ulcerans , we provide a framework for targeted, timely public health interventions. This integrated wildlife-environment-human approach exemplifies the power of One Health surveillance to detect and manage emerging zoonotic threats, offering a scalable model for other regions where Buruli ulcer is endemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".