Travel-related importation risk of mpox from Hong Kong to Shenzhen in 2023: A modeling study
Bibliographic record
Abstract
Mpox, a viral zoonotic disease formerly known as monkeypox, has gained global attention following a multi-country outbreak in 2022-23, primarily linked to close intimate contact. In China, mpox cases surged in June 2023, with nearly a quarter of new cases concentrated in Guangdong Province, particularly Shenzhen. This study aimed to estimate the importation risk of mpox cases from Hong Kong to Shenzhen in 2023, utilizing cross-regional population mobility data from January to October 2023. The analysis focused on local transmission in Hong Kong and the probability of mpox importation into Shenzhen. Results revealed a significant importation risk, with over a 50 % chance of at least one travel-based mpox case from Hong Kong in June 2023. The study underscores the necessity of enhancing inbound surveillance for travelers from high mpox prevalence regions. It is suggested that regional governments implement tailored strategies, including enhanced surveillance and dynamic risk assessment for effective cross-border disease management, supported by robust monitoring and coordinated actions across jurisdictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".