From legacy systems to real-time response: VPD-SMART implementation and its impact on public health surveillance in Paraguay and the Americas
Bibliographic record
Abstract
In the Americas, traditional public health surveillance systems often face a significant data-use gap, hindering a timely response to vaccine-preventable diseases (VPDs). This manuscript details the implementation of VPD-SMART, a novel DHIS2-based system, as a digital transformation initiative to enhance VPD surveillance and bridge this gap. We analyze Paraguay's transition from the legacy Integrated Surveillance Information System (ISIS) to VPD-SMART, focusing on how its functionalities, including real-time decentralized data collection and enhanced analysis, improve the performance of health information systems. We find that the transition to VPD-SMART significantly improved data quality, consistency, and timeliness. A quantitative analysis showed a notable increase in data completeness and a rise in consistency for key variables from 54 % to 97 %. The average time for data entry also decreased, shifting from a weekly to a daily basis. Qualitative findings confirm that the system empowers health authorities with real-time, data-driven insights. By examining these challenges and opportunities, we provide empirical evidence on how leveraging DHIS2 can enhance public health surveillance and inform similar digital transformation efforts in other low- and middle-income countries.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".