<strong>The seasonality of upper respiratory tract infections and their relationship to asthma</strong>
Bibliographic record
Abstract
The seasonality of upper respiratory tract infections and their relationship to asthma Alejo1 and D. Quesada2 1 Miami Dade College, Wolfson Campus, Miami FL 2 School of Science, Technology, and Engineering Management, St. Thomas University, Miami Gardens, FL The impact of weather conditions on both human health and the spread of diseases is a question addressed by Biometeorology and the answer is very important for health management and disease control. Upper respiratory tract infections due to different viruses (respiratory syncytial virus (RSV), rhinovirus influenza, human metapneumovirus) and bacteria (corynebacterium diphtheriae, chlamidia pneumonia) show seasonal patterns, mostly associated with the changes in the immune response in different seasons. Such infections often trigger asthma episodes that might be difficult to treat, especially in elderly and young children. This project is aimed at describing and modeling the pattern of seasonality in South and Central Florida due to both asthma and upper respiratory tract infections, when each one is considered the primary diagnosis at the Emergency Room (ER). The Department of Health of Florida via the Florida Asthma Coalition provided the health data used in this study. As a result of the statistical analysis, the peak of seasonality for Central and South Florida is centered in late January and early February. Compared with the rest of continental USA, this shift in time is associated with cold conditions arriving to South Florida in these periods. Cold and dry air is affecting the lining of epithelial cells from the respiratory tract in addition to the thermal stress due to the convective loss during respiration. Together, these conditions affect the response of the immune system and facilitate the reproduction of infective agents. A mathematical model based on the SEIR epidemic model is adapted to account for both, recovery from infections and a further development of asthma symptoms. The results might be very beneficial to medical practitioners as well as the pave the way to study potential effects due to global climate changes and the spread of different vector-borne diseases in addition to the spread of allergies.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".