Epidemiology of Influenza: A Diagnostic Lab based observational study
Bibliographic record
Abstract
Background: Influenza is a highly infectious respiratory disease of viral origin. It's a public health concern worldwide as well in India. The annual attack rate of influenza is estimated to be 5-10% in adults and 20-30% in children. Worldwide annual epidemics of influenza results in 3-5 million cases of severe illness and 250,000-500,000 deaths. The objective of this study is to understand the recent epidemiological behaviour of influenza virus by using data from diagnostic labs across states of Maharashtra and Delhi (India). Methods & Materials: A diagnostic lab-based data of 5950 subjects who were tested for influenza between 1st January 2015 to 5th October 2017 across Delhi, Mumbai, and suburbs of Mumbai (Thane, Goa, and Pune) was analysed. It's a descriptive observational retrospective study. The confidentiality of subjects is maintained, and no personal identifying information is used in the analysis. Results: Sample population has almost equal male and female proportion (49.4%) with 34.9 years mean age. Overall positivity for type A was 40.2% while for H1N1 was 26%. For type B positivity was 3%. The quarterly trends show third quarter of the year (July-Sept.) had the highest positivity except during 2017, where 2nd quarter has shown the highest rate of influenza (A and B collectively) positivity. Even during 2017, the third quarter has recorded high positivity rate (45.0). The third quarter in India corresponds to the rainy season, while second quarter to summer. Chadha et al study has shown peaks during monsoon season (July-sept) in Delhi and Pune. Area wise trends of Influenza shows less positivity for type A and for type B (0.6%) in Delhi. Navi Mumbai has shown overall high positivity for A, B and H1N1. On the comparison between Mumbai and Delhi, overall positivity (42.1%), for type A (40.5%), B (3.2%) and H1N1 (26.8%) was higher than Delhi. Conclusion: Big data-based research studies can be very helpful in understanding the epidemiological behaviour of diseases like influenza. A well-connected network of diagnostic labs can be a smart surveillance system to alert health care system timely in case of epidemics and pandemics of influenza.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".