Evaluation of Prevalence Patterns of Dengue Fever in Lahore District through Geo-Spatial Techniques
Bibliographic record
Abstract
Dengue and its impacts are growing environmental, economic and health concerns in Lahore. Disease pattern is important to know for better control and effective management, GIS is one of the tested tools and quite efficient for this purpose. In this study, firstly month-wise dengue cases mapping for seven consecutive years (2007-2013) is performed in order to reveal temporal or seasonal pattern of dengue disease in Lahore district. Then a composite analysis was conducted using Inverse Distance Weighted (IDW) technique in order to show dengue most affected locations (towns) and in this analysis, all cases of the study period (2007-2013) were appended and visualized by IDW. Temporally, September (6548 cases) was the most dengue affected month of all years whereas February (4 cases) was marked as least affected throughout the dengue incidence period. Endemic Foci is noticed in 2011 most affected months. This cluster of disease is agglomerated near Ravi River and Densely Populated Towns, which further aggravated the incidence of dengue in economically deprived areas. Data Gunj Baksh town was the most affected town and IDW results showed that this town is composite endemic foci where cases were agglomerated most frequently. The reason of prevalence in this town would possibly be due to its more density of population and proximity of Ravi River.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".