Humanitarian led community-based surveillance: case study in Ekondo-titi, Cameroon
Bibliographic record
Abstract
BACKGROUND: Community-based surveillance (CBS) has been used successfully in many situations to strengthen existing health systems as well as in humanitarian crises. The Anglophone crisis of Northwest Southwest Cameroon, led to burning of villages, targeting of health personnel and destruction of health facilities which, in combination with distrust for the government services led to a collapse of surveillance for outbreak prone diseases. METHODS: We evaluated the ability of the CBS system to identify suspected cases of outbreak prone diseases (OPD) as compared to the facility-based surveillance, evaluated the timeliness of the CBS system in identifying an OPD, reporting of OPD to District Health Service (DHS) and timeliness in outbreak response. The paper also assessed the collaboration with the DHS and contribution of the CBS system with regards to strengthening the overall surveillance of the health district and also determine the interventions undertaken to contain suspected/confirmed outbreaks. RESULTS: In total 9 alerts of suspected OPDs were generated by the CBS system as compared to 0 by the DHS, with 8 investigated, 5 responses and 3 confirmed outbreaks. Average time from first symptoms to alert generation by the CBS system was 7.3 days. Average time lag from alert generation from the CBS to the DHS was 0.3 days which was essentially within 24 h. There was extensive and synergistic collaboration with the DHS. DISCUSSION: CBS generated a higher number of alerts than traditional outbreak reported used in the region, and had timely investigations and if appropriate, responses. Careful selection of CHWs with strong community engagement led to the success of the project, and the use of the mobile health team in situ allowed for rapid responses to potential outbreaks, as well as for feedback to CHWs and communities. CBS was also well utilized for identification of other events, such as displacement and malnutrition. CONCLUSION: In conflict settings, CBS can help in outbreak identification as well as other events, and a mobile health team is crucial to the success of the CBS due to the ability to rapidly response to generated alerts. The mobile health team provided timely investigation of 8 of 9 alerts generated. Collaboration with existing DHS structures is important for systems strengthening in such settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".