Knowledge Integration to Support Networking for Laboratory Preparedness and Response to Emerging Pathogens
Notice bibliographique
Résumé
IntroductionLaboratories play a critical role in facilitating timely recognition of and response to public health threats.However, capabilities and capacities vary widely among laboratories around the world.The scientific community recognizes that: 1) no single laboratory or network can effectively cover all health hazard threats and 2) connecting laboratories through networks enables scientific communities to harness and contribute their expertise in response to public health threats, while adding value and enhancing opportunities to enrich their own work.However, a consolidated and accessible inventory of laboratories that would enable this to happen does not exist.Public health laboratories serve the essential function of identifying etiologic agents of disease in an accurate and timely manner.However, the practicality and potential of these laboratories in the detection, monitoring, and reporting of threats over a wider geographic range is limited by unclear case definitions, inadequate laboratory capacity, and often, limited political will of local authorities to comply with International Health Regulations (IHR) 2005 (Baker andFidler, 2006) to be prepared to respond to public health emergencies of international concern (PHEIC).Furthermore, some countries are not member states of World Health Organization (WHO) and therefore have no obligation to comply with IHR.Global, regional, and national laboratory networks serve to alleviate these issues by streamlining the detection, monitoring, and reporting procedures for communicable diseases in order to effectively and significantly reduce the global or regional burden of disease.Laboratory networks are useful in establishing and maintaining standards such as molecular disease confirmation by providing member laboratories with standardized testing and reporting procedures, reagents, equipment, training, reference materials, quality control indicators, and technical support.Such collaboration between and among laboratories facilitated by networks allows for rapid and accurate provision of information regarding the magnitude of disease and the strains that are circulating in particular regions, leading to faster response and more effective control of the threat.Some example networks include the Global Polio Network (Hull et al, 1997) and the Global Measles and Rubella Network (Featherstone et al, 2003). www.intechopen.com New Research on Knowledge Management Technology 214Although significant scientific knowledge has been developed, tested and translated into successful public health interventions and leading to reduced infectious disease burden, the public remains vulnerable to epidemics and pandemics.Emerging infections such as HIV, SARS, avian influenza and the recent H1N1 pandemic are further exemplified by the emergence and global spread of multi-drug resistant pathogens, which threatens our ability to treat viral and bacterial infections in hospitals and in the community.Taken together these acts of nature have put enormous pressure on governments to act quickly to protect the public's health.The public have in turn, incurred high costs in terms of lives and implementation of countermeasures.These public health problems have resulted in society disturbance, economic loss and political expectations.
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 | 0,007 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,010 |
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 ».