The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to assess model structures, performance metrics, and dataset characteristics. The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. Random Forest algorithms demonstrated superior performance in disease outbreak prediction, with mean ACC scores of 0.85. Iran, Saudi Arabia, and Egypt emerged as regional leaders, collectively contributing 54 % of the analyzed studies. The temporal analysis showed peak research output in 2022 (n = 30 studies), followed by a 25 % decline in 2023. Despite promising performance, challenges such as data quality, infrastructural limitations, and algorithmic bias persist. This review highlights the need for standardized protocols, enhanced digital infrastructure, and collaborative efforts to realize the full potential of ML in enhancing public health interventions across the region. Future research directions should prioritize multi-center validation studies, standardized reporting frameworks, and integration of diverse data modalities to enhance model robustness and clinical applicability.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it