Trends, seasonality and forecasts of pulmonary tuberculosis in Portugal
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) is a global public health concern. Surveillance programmes present invaluable epidemiological information regarding its temporal evolution, particularly for pulmonary tuberculosis (PTB), the most common form of TB and the one that presents the greatest challenge in public health. OBJECTIVES: To characterise, model and predict monthly incidence rates for PTB in Portugal disaggregated by high/low-incidence areas, sex and age groups. DESIGN: PTB monthly incidence rates were estimated based on PTB cases diagnosed in 2000-2010, disaggregated by population and geographic characteristics. Seasonal-trend LOESS (STL) decomposition was employed to model trend and seasonality. Seasonal autoregressive integrated moving average (SARIMA) models were fit to characterise series behaviour and forecast PTB monthly incidence rates. RESULTS: Overall, the time series showed a downward trend in and seasonality of PTB diagnosis, with a peak in March and a trough in December. The mean seasonal amplitude was consistently higher in high-incidence areas, in males and in adults aged 25-54 years. SARIMA models were found to adequately fit and forecast the time series, thus predicting trend and seasonal persistence. CONCLUSIONS: STL and SARIMA findings concurred and were accurate. Endemic PTB seems to be slowly declining and case diagnosis is likely seasonal, which can be expected to persist if past conditions continue.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".