Analysis and Forecasting of Two Time Series Models for Respiratory Infectious Diseases
Bibliographic record
Abstract
The global impact of the COVID-19 pandemic has heightened attention on respiratory diseases and medical infrastructure across the globe.This study focuses on the comparison of healthcare systems in Japan, a developed country, and Brazil, a developing nation, to explore how differences in medical systems affect the distribution of healthcare resources and outcomes in respiratory disease management.Given the more extensive medical coverage in Japan compared to Brazil, this research investigates whether Japan demonstrates better outcomes in terms of lower mortality rates for respiratory diseases.Utilizing data from the World Health Organization (WHO), this study conducts a time series analysis for upper and lower respiratory tract infections, applying and comparing various predictive models.The Exponential Triple Smoothing (ETS) model showed superior fit and forecasting accuracy.The results indicate that Japan consistently maintains lower mortality rates compared to Brazil, suggesting that disparities in healthcare systems significantly influence disease outcomes.These findings underscore the importance of robust healthcare infrastructure in managing respiratory diseases, particularly in the context of a global pandemic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".