Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d’Ivoire: Cross-Sectional Survey Among Users
Bibliographic record
Abstract
Background: Accurate and timely infectious disease surveillance is pivotal for effective public health responses. An important component of this is the disease surveillance tools used. Understanding views and experiences of users is crucial for informing policy decisions and ensuring the seamless functioning of surveillance systems. Objective: In this study, we aimed to assess the user perceptions of 3 disease surveillance tools used in Côte d'Ivoire, namely, MAGPI, District Health Information Software 2 (DHIS2), and Surveillance Outbreak Response Management and Analysis System (SORMAS), the latter was implemented in 2021 within a pilot scheme. Methods: We conducted interviews and a web-based survey distributed to users of the 3 surveillance tools. The survey assessed users' views of the surveillance tools' usefulness, ease of use, feelings toward the tool, conditions that may influence the use, and other characteristics. The descriptive analysis compared responses from SORMAS, MAGPI, and DHIS2 users, providing a comprehensive evaluation of their experiences. Results: Among the 159 respondents who actively use one of the systems, MAGPI was the most widely used surveillance tool among respondents (n=127, 79.9%), followed by DHIS2 (n=108, 67.9%), and SORMAS (n=25, 15.7%). In terms of users' perceptions, SORMAS, despite its limited implementation, emerged as a tool that allows for data analysis and had the most comprehensive set of functionalities. DHIS2 was appreciated for its frequency of report provision, although users reported occasional IT system failures. MAGPI was recognized for its ease of use but was reported to lack certain functionalities offered by the other surveillance systems. Conclusions: This study offers valuable insights into the perceptions of disease surveillance tools users in Côte d'Ivoire. While all systems were positively regarded, each exhibited strengths and weaknesses addressing different needs and functionalities. Policy makers and health officials can use these findings to enhance existing tools or consider a unified approach for infectious disease surveillance systems. Understanding users' perspectives allows them to optimize the choice of surveillance tools, ultimately strengthening public health responses in Côte d'Ivoire and potentially serving as a model for other countries facing similar decisions in their health care systems.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".