Development of an Ontology-Based Solution to Reduce the Spread of Viruses
Bibliographic record
Abstract
With the sudden emergence of many dangerous viruses in recent years and with their rapid transmission and danger to individuals, most countries have adopted several strategies, such as closure and social distancing, to control the spread of the virus in the population. In parallel with all these precautions, scientific laboratories are working on developing the appropriate vaccine, which in many cases takes many years. Until then, it is necessary to resort to many solutions, including solutions that rely on information technologies and artificial intelligence (AI). In this context, this paper proposes a new solution based on the ontology and rules of intelligent reasoning. Initially, the virus environment is analyzed, followed by the extraction and editing of the main elements of the ontology using the “Protégé” software. In the last step, the proposed solution is tested, by choosing the city of Adrar in southwestern Algeria, which was particularly affected by COVID-19. Three scenarios were shown for different cases. The efficiency of the proposed solution was confirmed through the instructions it provides in the event of symptoms appearing in a person. In addition, this solution helps the competent authorities know the location and extent of the epidemic by informing the local communities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".