An application of spatial analysis and GIS in Tuberculosis (TB) cases in Central Luzon, Philippines
Bibliographic record
Abstract
According to WHO (2022), tuberculosis (TB) is the primary cause of ill health and the leading cause of death globally. It is estimated that approximately a quarter of the world’s population has been infected. With 372,367 cases of tuberculosis in 2022, the Philippines is among the top eight countries that accounted for more than 66% of all TB cases worldwide. Region III, along with NCR and Region IV-A, is one of the regions in the Philippines with the highest incidence of TB. This paper utilized the geographical information system (GIS) for easier visualization, and Getis-Ord Analysis, a type of spatial analysis tool for quick interpretations to provide an evidence-based framework for TB response. The spatial analysis was conducted to (1) determine if there are clusters of TB cases in the region across various periods and (2) determine if there are hot spots of TB cases in the most recent TB data covering 2019, 2020, and 2021. The results indicate that only the 2019 TB cases exhibit significant non-random clusters. It is recommended that further investigation be conducted to determine if the spatial clustering in 2019 is associated directly or indirectly with the El Niño event that occurred that year. On the other hand, the non-significance of the results for the years 2020 and 2021 may be attributed to the underreporting due to the implemented health protocols implemented to minimize the spread of COVID-19 which affected the accuracy of the reported cases. The results of the paper may be used for optimal resource allocation in addressing the spread of the disease.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".