CASE 6: A Rapid Risk Assessment Tool: Determining the Risk of New/Emerging/Re-Emerging Infectious Diseases in Canada
Notice bibliographique
Résumé
The protagonist, Dr. Anna Moreno, is an epidemiologist at the Public Health Agency of Canada (PHAC), where she manages the Public Health Risk Sciences Department. Her role includes managing monthly meetings and advising when it is necessary to use the Rapid Risk Assessment (RRA) tool to conduct an analysis of any emerging infectious disease relevant to Canada. The case presents a fictitious situation in which a series of outbreaks of an influenzalike infectious disease have occurred in certain regions of the Americas. In response to outbreaks of severe respiratory and influenza-like symptoms within the Americas and the more than 100 associated deaths over the past month, the World Health Organization has declared the unknown infectious disease a Public Health Emergency of International Concern (PHEIC). Anna has a meeting that morning, and she will need to prepare an approach to create a working group responsible for conducting an RRA for this unknown disease. Challenges arise because the existing scientific evidence and literature about the disease is limited and Anna will need to defer to the expert knowledge of her team while minimizing expert opinion bias. Given the general standards of RRAs, the assessment should be conducted within the next 24 to 48 hours. Knowing that the outcomes of the risk assessment will set the landscape for the PHAC’s response to the PHEIC, Anna and her team will need to ensure the assessment is conducted in a timely and efficient manner. The results of the RRA will be presented to upper management before being disseminated to the general public. Finally, the case includes a case study, based on true events, of the initial application of the tool to the 2015 Zika virus disease outbreak. Although the initial assessment led to the conclusion that Canadians were at minimal risk, unexpected subsequent Zika outbreaks in 2016 revealed the need to refine and adapt the RRA tool. This case study emphasizes the importance of completing a risk assessment at multiple time points throughout the course of a disease to capture the complexities of evolving information and circumstances.\nThe pedagogical value of presenting this case is rooted in presenting a real-world situation and exposing students to the complexities of translating foundational public health practices to social contexts that do not allow for typical solutions. Although it is necessary to learn concrete knowledge in a classroom environment, this information serves as a foundation to build on through experience within the public health field. Being immersed in real-world situations is imperative for enabling students to visualize how this knowledge may not translate perfectly during a public health event. This case will complement the focus on emergency preparedness and monitoring and managerial control mechanisms emphasized in the course MPH 9010- Managing Health Services. This level of public health response provides direction and recommendations for all sectors in the face of urgent events, such as infectious disease outbreaks.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,011 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».