Satellite-Based Exposure Model for Predicting Fine Particulate Matter (PM2.5) to Assess its Role on the Incidence of Acute Lower Respiratory Infections (ALRI) in Infants of Rural Bangladesh
Bibliographic record
Abstract
Ambient fine particulate matter (PM2.5) has been shown to increase the risk of Acute Lower Respiratory Infections (ALRI), the burden for which is much higher in children of low-and middle-income countries (LMICs) such as Bangladesh where poor air quality is a significant public health concern. One challenge in assessing health effects associated with PM2.5 in LMICs is quantifying exposures for affected populations as measurement data are sparse. To address this issue, spatiotemporal regression and machine learning models that leveraged satellite observations of Aerosol Optical Depth (AOD) and meteorology to predict daily PM2.5 exposures between 2017 and 2021 at the georeferenced residential locations of children that were part of the Bangladesh Cook Stove Pregnancy Cohort Study (BCSPCS) were developed. The eXtreme Gradient Boosting (XGB) Machine Learning Model showed superior predictive performance in validation tests. Predicted exposures linked to the BCSPCS revealed that children having ALRI were exposed to statistically significantly higher PM2.5 than those without ALRI. This thesis demonstrates the feasibility of using satellite-AOD derived PM2.5 exposures to study health effects in LMICs and shows that poor air quality is a significant risk factor for the development of childhood ALRI.
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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 | 0.001 |
| 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 | 0.001 |
| 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".