Improved Spatial-Temporal CAR Models for Dengue Fever Incidence: Evidence from Banyumas
Bibliographic record
Abstract
In the last six years, dengue fever cases in Banyumas Regency in 2024 were very high. The relative risk (RR) of dengue hemorrhagic fever (DHF) is of interest. In this paper, we proposed the Spatial Temporal-Conditional Autoregressive (ST-CAR) model to analyze the association of DHF with related factors. In addition, we improve the model with offset modification on ST-CAR. The results of research showed that the best ST-CAR model was the interaction of intrinsic CAR and independent identically distributed temporal effects. In addition, the offset modification in the ST-CAR model resulted in the smallest Watanabe-Akaike Information Criterion (WAIC). Based on the study's findings, the two highest RR from year to year are located not far from the city center (North Purwokerto) and the tourist attraction (Baturaden). Both locations are often associated with higher risk factors such as population density and greater social interaction, which facilitate transmission.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".