Epidemiological characteristics and spatiotemporal patterns of hand, foot, and mouth disease in Hubei, China from 2009 to 2019
Bibliographic record
Abstract
BACKGROUND: Hand, foot, and mouth disease (HFMD) is a public health issue in Hubei and studies of- spatiotemporal clustering at a fine scale are limited. The purpose of this research was to analyze the epidemiological characteristics, temporal variation characteristics, and spatiotemporal clustering of HFMD cases at the town level from 2009 to 2019 to improve public health outcomes. METHODS: Mathematical statistics, a seasonal index, wavelet analysis, and spatiotemporal scans were used to analyze epidemiological characteristics, time series trends, and spatiotemporal clusters of HFMD in Hubei. RESULTS: EV-A71 (Enterovirus A71) and CVA16 (Coxsackievirus A16) constitute the two primary pathogens of the HFMD epidemic in Hubei, among which EV-A71 is the dominant pathogen, especially in 2016. In terms of age distribution, a major peak occurred at 0-5 years and a very small increase appeared at 25-35 years, with the former having a higher incidence among males than females and the latter having the opposite difference between males and females. The number/rate of HFMD cases exhibited a considerable increase followed by a moderate decline from 2009 to 2019, with the first large peak in April-July and a smaller peak in November-December. HFMD in Hubei exhibited the characteristics of a 270-day cycle with multiscale nesting, which was similar to the periodicity of HFMD cases caused by EV-A71 (9 months). Cities with a higher incidence of HFMD formed a part of an "A-shaped urban skeleton". Subdistricts had the highest incidence of HFMD, followed by towns and villages. The spatiotemporal scan results showed one most likely cluster and 22 secondary clusters, which was consistent with the geographic location of railways and rivers in Hubei. CONCLUSIONS: These findings may be helpful in the prevention and control of HFMD transmission and in implementing effective measures in Hubei Province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".