A Smartphone-Based Application Improves the Accuracy, Completeness, and Timeliness of Cattle Disease Reporting and Surveillance in Ethiopia
Bibliographic record
Abstract
Accurate disease reporting, ideally in near real-time, is a prerequisite to detecting disease outbreaks and implementing appropriate measures for their control. This study compared the performance of the traditional paper-based approach to animal disease reporting in Ethiopia to one using an application running on smartphones. In the traditional approach the total number of cases for each disease or syndrome were aggregated by animal species and reported to each administrative level at monthly intervals; while in the case of the smartphone application demographic information, a detailed list of presenting signs, in addition to the putative disease diagnosis were immediately available to all administrative levels via a Cloud-based server. While the smartphone based approach resulted in much more timely reporting, there were delays due to limited connectivity; these ranged on average from 2 days (in well-connected areas) up to 13 days (in more rural locations). We outline the challenges that would likely be associated with any widespread roll-out of a smartphone-based approach such as the one described in this study, but demonstrate that in the long run the approach offers significant benefits in terms of timeliness of disease reporting, improved data integrity and greatly improved animal disease surveillance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".