PA-769 Bayesian spatio-temporal analysis of malaria hotspot in Gabon from 2000 to 2015
Bibliographic record
Abstract
Background At the local level, malaria transmission persists through hotspots. Besides other known factors, the distribution of malaria hotspots may be shaped by environmental variables. However, research focusing on this aspect has been relatively scarce in Gabon. This underscores the need for further investigations to elucidate the specific environmental factors together with a specific intervention, that may contribute to the distribution of malaria hotspots, taking into account the spatio-temporal effect in Gabon. Methods These data were part of the Demographic Health Survey program from 2000 to 2015. Hotspots of malaria prevalence for cluster of households were identified using the local Getis-Ord Gi* statistic. The effect of covariates on the outcome was assessed using a Bayesian space-time framework with a Binomial model, implemented in the Integrated Nested Laplace Approximation (INLA), using the Stochastic Partial Differential Equations approach (SPDE). Results A total of 316 clusters were initially considered, out of which 257 clusters with known hotspot status were included in the analysis. Among these clusters, approximately thirty percent were persistent hotspot over time and concentrated in rural areas. Using a spatio-temporal model, association between malaria prevalence hotspot variation and two key factors was found: years and rainfall. Each additional year or amount of rainfall was associated with an increase in the odds of hotspot occurrence (adjusted posterior odds ratio [AOR]: 1.32, 95% confidence interval [CI]: 1.03–1.69 and AOR: 1.15, 95% CI: 1.02–1.30, respectively). Furthermore, the analysis found that clusters of households with high insecticide-treated net (ITN) coverage were less likely to be hotspots (0.19 (95% CI: 0.06–0.61)). Conclusion These findings highlight the spatio-temporal dynamics of hotspots and the role of the rainfall, in influencing their occurrence. Moreover, the protective effect of high ITN coverage suggests the importance of targeted interventions in mitigating hotspot formation and malaria transmission.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".