Using PCA, Poisson and Negative Binomial Model to Study the Climatic Factor and Dengue Fever Outbreak in Lahore
Bibliographic record
Abstract
Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. The study aims to understand, explore and compare the climatic factors of Karachi and Lahore that causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. Principal component analysis (PCA) is performed for the purpose of finding if there is/are any general environmental factor/structure which could be considered as Pakistani climate. We developed an early warning model for the prediction of dengue outbreak in Lahore. This has been done by using Poisson regression and Negative binomial regression model. For this purpose we use daily, weekly and monthly data of Lahore. The negative binomial model with lag (28) for Lahore daily data for climatic variable is best model. Lahore daily and weekly maximum temperature effect negatively and for the past 28 days it is estimated to negatively influence on the dengue occurrence by 26.1% times. Daily wind speed is effecting negatively by 14.7% times and minimum temperature effect positively for the past 28 days by 86.7%times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".