Machine learning for predicting measles outbreaks in resource-limited settings
Bibliographic record
Abstract
Abstract Background Measles remains a major public health issue, especially in regions with low vaccination rates and limited healthcare access. Timely outbreak prediction is crucial for effective intervention and resource allocation. Traditional epidemiological models struggle with real-time predictions due to reliance on historical data and limited analysis. Artificial intelligence (AI) offers a data-driven approach to capturing complex outbreak patterns and improving prediction accuracy. Methods This study developed and evaluated machine learning (ML) models to predict measles outbreaks at the community level, using real-world data collected by community volunteers in Ethiopia. The dataset includes demographic, vaccination status, location, clinical, and image data, providing a comprehensive assessment of outbreak risk factors. Five ML models-Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)-were assessed using metrics such as area under the receiver operating curve (AUC), precision, recall, and F1-score. Results The results demonstrate that machine learning models can effectively predict measles outbreaks, with the top-performing model, K-Nearest Neighbors (KNN), achieving an AUC of 0.87 (95% CI: 0.84-0.90). Among the five models tested, KNN outperformed the others, showing the highest predictive accuracy, and the difference in AUC was statistically significant (p < 0.05). Conclusions The model's ability to capture complex epidemiological factors underscores its potential to enhance surveillance and early warning systems. This research highlights the effectiveness of ML-based measles prediction in resource-limited settings. Integrating these models into public health strategies can improve early detection and reduce measles impact. Future work will focus on refining the model, incorporating real-time data, and expanding evaluation metrics for practical deployment. Key messages • Machine learning enables accurate early measles detection fusing different data sources, supporting faster response and better health outcomes in communities. • Machine learning offers a powerful tool for predicting outbreaks early, filling critical gaps in low-resource health systems.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".