Exploring Geospatial Clusters of Five Primary Respiratory Diseases in South Punjab, Pakistan: An Epidemiological Examination
Bibliographic record
Abstract
Respiratory diseases constitute a significant burden of morbidity and mortality in developing nations such as Pakistan. This study aims to analyze five prevalent respiratory ailments - acute respiratory infections (ARI), tuberculosis (TB), pneumonia, asthma, and chronic obstructive pulmonary diseases (COPD) - within South Punjab, Pakistan. Utilizing the tehsil level (the administrative sub unit of district), case data spanning five years (2016-2020) were collected from 1,487 government health centers across the study area. Spatial analysis techniques including Local Moran’s I and Getis Ord Gi* statistics were employed to identify clusters and outliers. The results revealed spatial heterogeneity in respiratory disease prevalence, delineating both high-intensity (hotspots) and low-intensity (cold spots) clusters across the region. Specifically, ARI hotspots were observed in northeastern and central regions, asthma hotspots in central and north-central areas, COPD hotspot areas in the north and northeast, pneumonia hotspots in the central region, and TB hotspots predominantly in the central region. These findings offer critical insights for targeted public health interventions, facilitating resource allocation for disease prevention and control efforts. Additionally, this study presents recommendations addressing local environmental and socio-economic factors to mitigate respiratory disease incidence through administrative environmental management and community engagement strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".