Probable airborne transmission of <i>Burkholderia pseudomallei</i> causing an urban outbreak of melioidosis during typhoon season in Hong Kong, China
Bibliographic record
Abstract
Between January 2015 and October 2022, 38 patients with culture-confirmed melioidosis were identified in the Kowloon West (KW) Region, Hong Kong. Notably, thirty of them were clustered in the Sham Shui Po (SSP) district, which covers an estimated area of 2.5 km2. Between August and October 2022, 18 patients were identified in this district after heavy rainfall and typhoons. The sudden upsurge in cases prompted an environmental investigation, which involved collecting 20 air samples and 72 soil samples from residential areas near the patients. A viable isolate of Burkholderia pseudomallei was obtained from an air sample collected at a building site five days after a typhoon. B. pseudomallei DNA was also detected in 21 soil samples collected from the building site and adjacent gardening areas using full-length 16S rRNA gene sequencing, suggesting that B. psuedomallei is widely distributed in the soil environment surrounding the district. Core genome-multilocus sequence typing showed that the air sample isolate was phylogenetically clustered with the outbreak isolates in KW Region. Multispectral satellite imagery revealed a continuous reduction in vegetation region in SSP district by 162,255 m2 from 2016 to 2022, supporting the hypothesis of inhalation of aerosols from the contaminated soil as the transmission route of melioidosis during extreme weather events. This is because the bacteria in unvegetated soil are more easily spread by winds. In consistent with inhalational melioidosis, 24 (63.2%) patients had pneumonia. Clinicians should be aware of melioidosis during typhoon season and initiate appropriate investigation and treatment for patients with compatible symptoms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".