Epidemiological Aspects of Human Brucellosis and Leptospirosis Outbreaks in Korea
Bibliographic record
Abstract
UNLABELLED: In order to compare the epidemiological aspects of human brucellosis (HB) and human leptospirosis (HL) outbreaks in Korea, we have analyzed the current state both of the disease incidence and related risk factors. A total 651 cases of HB occurred between 2001 and 2010 in Korea, and the average incidence rate per 100,000 populations was 0.15. A total 1,153 of HL cases occurred during the same period, and its rate was 0.24. While most of the HB prevalence occurred in the whole year round, prevalence in summer was more prominent for HB (p < 0.01), while outbreaks in autumn was more frequent for HL (p < 0.01). Geographical distribution HB cases were eastern and western regions of the rural (88.6% of total) in the Korean peninsula, showing higher outbreaks than other areas, while HL occurred in easterly regions (64.5%). Significantly more males were infected in both HB (86.8%) and HL (59.5%) than those of females in both HB (13.1%) and HL (41.1%), respectively (p < 0.01). The distribution by age groups were different between HB and HL, while the outbreaks over 62.1% of the cases of HB occurred in 40- to 59-year-old age group, and that of HL was clearly showing a high incidence in the elderly age over 60-year-old (59.6%) (p < 0.01). In both diseases, elderly people especially in famers showed a very high incidence rate (57.9% of HB and 52.2% of HL), which is possible due to increased outdoor activities and a decreased number of young people in those areas. The occupational distribution of HB cases was famers, veterinarians, dairyman and others, and those of HL cases were broad. In conclusion, the difference between HB and HL risk factors reflects the different influence of host/vector, climate, and geographical and environmental characteristics in the epidemiological patterns. KEYWORDS: Human brucellosis and leptospirosis; Epidemic aspects; Risk factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".