Epidemiological analysis on public health emergencies in Renhe District of Panzhihua City from 2004-2012
Bibliographic record
Abstract
[Objective]To analyze the epidemiological characteristics of public health emergencies in Renhe District of Panzhihua City,provide a scientific basis for preventing and controlling public health emergencies.[Methods]The network reporting data of public health emergencies in Renhe District of Panzhihua City from 2004-2012 were analyzed by the descriptive epidemiological method.[Results]A total of 14 public health emergencies were reported during 2004-2012,8 943 people were involved,and there were 549 patients,with the attack rate of 6.14%.5 cases died,with the fatality rate of 0.91%.There were 4 events of large-level,7 events of general-level,and 3 unclassified events.The peak seasons were the second quarter and the third quarter.64.29% of emergencies were outbreaks of infectious diseases,and 35.71% were food poisoning.The attack rate and fatality rate of food poisoning were the highest,which was 47.06% and 3.85% respectively.Public health emergencies occurred mainly in rural area(71.43%),and most of victims were the students(81.42%).11(78.57%) emergencies were diagnosed by laboratory detection.The events of infectious diseases were reported within 8.66 days averagely,while the events of food poisoning were reported within 15 day.[Conclusion]Strengthening the management of respiratory infectious disease in schools,food poisoning and zoonotic diseases in rural families,as well as improving the supporting effect of laboratory detection are the main measures for effectively preventing and controlling public health emergencies in Renhe District.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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 teacher head, 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".