COVID-19 Regional Safety Assessment Using Evaluation Based on Distance from Average Solution (EDAS) Method
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
The process of assessing the safety and risk level of a particular region or area in respect to the COVID-19 pandemic is known as COVID-19 Regional Safety Assessment. It involves analyzing various factors, such as the number of active cases, testing and reporting capabilities, vaccination rates, healthcare system capacity, implementation of public health measures, travel restrictions, presence of variants of concern, and localized outbreaks. A complete evaluation of regional safety is necessary for public health professionals, legislators, and residents to successfully prevent the spread of COVID-19 and protect public health and wellbeing. Authorities may identify areas of concern, distribute resources wisely, and put targeted measures in place to restrict the virus's spread by performing a thorough examination. In order to restrict the virus's spread and protect the health and welfare of communities, it is crucial for guiding decision-making processes, identifying problem areas, and effectively allocating resources. The research carried out through regional safety assessments advances our knowledge of the pandemic, guides public health initiatives, and encourages the use of evidence-based decision-making in order to effectively battle COVID-19. Distance from Average Solution-Based Evaluation (EDAS)The evaluation based on distance from the average solution approach assesses the efficacy or quality of individual solutions or data points by comparing each solution or data point to the average or mean solution. This approach is commonly employed in various fields, including optimization, data analysis, and decision-making.In this evaluation method, the average solution serves as a reference point or baseline. It is crucial to remember that the evaluation's specific context and goals may influence the choice of the average solution and distance metric. Additionally, other evaluation criteria or metrics may be employed in conjunction with the distance-based evaluation to obtain a more comprehensive assessment of the solutions. China, Denmark, Germany, Hong Kong, Hungary, Israel, Australia, Austria, Canada, and Efficiency of the government, monitoring and detection, and quarantine Emergency Preparedness, regional resilience, and healthcare readiness .Ranking of the nation based on the Covid-19 Regional Safety Assessment survey. Hungary is shown as occupying the last slot, whereas China is listed as occupying the first spot. It has been noted that China has a significant influence on COVID-19.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle