Modelling of potential risk areas of pertussis cases in the Philippines using bioclimatic envelopes
Bibliographic record
Abstract
OBJECTIVES: Pertussis remains a pressing public health concern in the Philippines despite being vaccine-preventable. The resurgence of the disease, driven by waning immunity, vaccine hesitancy and erratic outbreaks, underscores the need for innovative surveillance methods. Thus, this study intends to create a predictive model of potential risk areas for pertussis outbreaks in the Philippines using bioclimatic variables. METHODS: This study employs the maximum entropy algorithm to predict pertussis risk areas in the Philippines based on 19 bioclimatic variables. The occurrence data of reported pertussis cases were obtained from two sources: the National Disaster Risk Reduction and Management Council, covering 30 March-11 June 2024, and the Weekly Epidemiological Surveillance Report, spanning 1 January-12 October 2024. RESULTS: Key variables such as Mean Temperature of the Wettest Quarter (BIO8), Precipitation of the Wettest Quarter (BIO16) and Precipitation of the Driest Quarter (BIO17) were identified as significant predictors. Results revealed high-risk areas concentrated in northern Luzon, particularly Central Luzon (Region III), the major coastal areas of Ilocos (Region I), National Capital Region, MIMAROPA (Region IV-B), and an isolated area in Bicol (Region V). The model performance indicates excellent predictive accuracy (AUC = 0.972). CONCLUSION: The findings highlight how climatic factors shape pertussis distribution through anthropogenic means (e.g., higher humidity increases the chances of acquiring respiratory problems), providing a framework for eco-epidemiological risk assessment. This approach enhances targeted intervention planning, resource allocation, and early warning systems, particularly in resource-limited settings like the Philippines. The study underscores the role of Maximum Entropy in addressing re-emerging diseases, contributing to sustainable public health preparedness and mitigation strategies in tropical regions given that there is currently no predictive model for pertussis cases in the Philippines.
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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 | 0.000 | 0.001 |
| 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".