A review on health system-based surveillance for acute flaccid paralysis: technological advancements, challenges, and outlooks
Bibliographic record
Abstract
BACKGROUND: Poliomyelitis is a severe viral infection that can lead to permanent paralysis or death, primarily affecting children under 15 years of age. Acute Flaccid Paralysis (AFP) surveillance is essential for global polio eradication efforts, enabling the rapid detection, reporting, and investigation of suspected cases. Implementing various technological solutions has significantly advanced AFP surveillance, which is the gold standard for early polio case identification. These technologies include short message alert systems for better case reporting, the Open Data Kit (ODK) for efficient data collection and management, digital specimen tracking systems for improved sample handling, geospatial mapping tools for monitoring surveillance coverage, and awareness campaigns to engage healthcare workers and communities. Despite these advancements, there remains a notable absence of comprehensive analyses evaluating the cumulative progress, challenges, and policy implications of AFP surveillance. MAIN BODY: This study presents a targeted literature review of English-language publications from 2002 to 2024, focusing on challenges, technological advancements, and practical recommendations for AFP surveillance. Only empirically grounded, methodologically sound studies centered on AFP within health systems were included, with preference for research conducted in polio-endemic countries. Studies lacking methodological rigor or direct relevance were excluded. The review highlights how digital reporting platforms, specimen tracking, geospatial monitoring, and awareness campaigns have strengthened surveillance operations. At the same time, it identifies persisting weaknesses in surveillance methodologies and gaps in coverage, underscoring the need for further innovation and integration into health systems. CONCLUSION: By synthesizing evidence on the latest developments, this review addresses a critical gap in the literature and provides actionable recommendations to improve AFP surveillance. The findings inform both practice and policy by identifying innovative solutions and future directions, ultimately contributing to stronger global health surveillance systems and advancing polio eradication strategies.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".