A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis
Bibliographic record
Abstract
The main objective of spatiotemporal analysis is to offer precise predictions of outcomes. The objective of this study is to assess the accuracy of the Bayesian Latent Gaussian Model in predicting outcomes by utilizing both time-varying and fixed spatial weight matrices. The results of the Monte Carlo simulation suggest that when there is moderate spatial autocorrelation (between 0.3 and 0.7), it is strongly advised to use a time-varying spatial weight matrix. This approach yields the most precise predictions and minimizes any distortion in parameter estimates. Furthermore, we provide an illustrative case study where we simulate the effects of exposure to multiple pollutants on tuberculosis. The analysis revealed that particulate matter 10 (PM10), nitrogen oxides (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), have a positive influence on the risk of TB, with spatial effects that change over time. The model demonstrates that a rise of 1 mg/m³ in the levels of PM10, NO2, SO2, CO, and O3 is linked to corresponding increases in TB cases by 2.1%, 21.17%, 13.20%, 6.72%, and 6.59%, respectively. NO2 and SO2 have the most significant influence on the risk of tuberculosis (TB). These findings enhance our comprehension of the spatial correlation of TB over time and promote further investigation to determine the most efficacious strategies for mitigating the dissemination of TB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".