Epidemiological investigation on hemorrhagic fever with renal syndrime in Pingyi county from 1980 to 2006
Bibliographic record
Abstract
Objective Analyze the prevalent trend of hemorrhagic fever with renal syndrome(HFRS)in Pingyi county,and provide scientific basis for prevention and control of the disease.Methods Epidemiological trends of HFRS in Pingyi county was analyzed with the method of descriptive epidemiology based on the HFRS case data and the surveillance information of host from 1980 to 2006.Results From 1980-2006,there were 9519 cases of HFRS were reported,the average annual incidence rate of this disease was 26.28/100 000,and 277 cases were died with the morbidity of 2.91%.The peak of incidence rate was from 1992 to 1998.The prevalence of HFRS varied periodically and the prevalence period was about 3-5 years,which mostly focused on the spring,fall and winter.It often occurred in low plains with rivers,and the patients were mostly the males who were young and strong.The prevalence trend gradually decreases,and its mortality in autumn and winter was higher than in spring.The rodent density was in the peak in the third quarter and the rate of rodent carrying virus was the highest in the fourth quarter.Conclusion The prevalence of HFRS in Pingyi county had the characteristic of periodicity,seasonal and multitudinous.In recent years,though the HFRS morbidity and incidence rate gradually decreased,the crowd morbidity,mice density and the rate of mouse carrying virus still maintained at the high level.In order to control HFRS,the integrated control measure must be taken including deratization in the spot and the inoculation of HFRS vaccine in the key crowd.
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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 | 0.002 | 0.001 |
| 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".