Leveraging AI in digital one health: an inter-university collaboration for emerging and re-emerging infectious disease control in Cameroon
Bibliographic record
Abstract
Emerging and re-emerging infectious diseases (ERID) pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak mitigation. This article explores collaboration between research teams based in the faculties of Health Sciences and Science of the University of Buea and the School of Veterinary Medicine and Science of the University of Ngaoundere (DigiCare Cameroon) for integrating artificial intelligence (AI) for early detection and management of ERID through a Digital One Health (DOH) approach. DigiCare is part of an interdisciplinary network called Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP) aimed at addressing pandemic and epidemic preparedness and response by strengthening more equitable and effective public health preparedness and response to infectious disease outbreaks in low- and middle-income countries. DigiCare is aimed at improving the health and well-being of the population through sustainable and effective solutions that protect lives and ensure a resilient future leveraging on the power of AI and DOH. DigiCare Cameroon was launched on November 23rd, 2023, at the University of Buea campus during an event graced by numerous high-ranking university and government officials from the public health, environmental, scientific research, and veterinary sectors, alongside representatives from civil society, researchers, students, and community leaders. Baseline data have been collected in communities to provide an evidence-based platform to develop applications that tailor AI towards health care delivery using integrated DOH approaches. This inter-university collaboration will ultimately contribute in strengthening the capacities of health systems to prepare, prevent and mitigate epidemics and pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".