Forecasting Model for the Number of Patients with Pneumonia in Thailand
Bibliographic record
Abstract
The purpose of this research was to construct the most suitable forecasting model for the number of patients with pneumonia in Thailand. The data gathered from the website of Social and Quality of Life Database System during the first quarter, 2003 to the fourth quarter, 2014 (48 values) were used and divided into two categories. The first category had 44 values, which were the data during the first quarter, 2003 to the fourth quarter, 2013 for the modeling by the methods of Box-Jenkins, Winters’ additive exponential smoothing, and combined forecasting. The second category had 4 values,which were the data from all four quarters in 2014 for checking the accuracy of the forecasting models via the criterion of the lowest mean absolute percentage error. The results showed that for all forecasting methods that had been studied, combined forecasting method was the most suitable for this time series and the forecasting model was Ŷt= 0.234904Ŷ1t + 0.765096Ŷ2t where Ŷ1t and Ŷ2t represented the single forecasts at time t from Box-Jenkins and Winters’ additive exponential smoothing, respectively. When using the combined forecasting method to predict the number of patients with pneumonia, we found that the number of pneumonia cases increased. However, the predictions of the first quarter may be lower than the actual value, so who used the forecasting model should be careful and if there were the current time series data, the model should be updated. Including should be considering the time series of monthly and weekly in order to construct the forecasting model.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".