Participatory epidemiology methods for foot and mouth disease surveillance.
Notice bibliographique
Résumé
Participatory epidemiology (PE) is the application of participatory rural appraisal techniques to epidemiological studies and disease surveillance. The use of PE techniques in disease surveillance is termed participatory disease surveillance (PDS), a decision-oriented approach for the collection of epidemiological intelligence. In PDS, surveillance is defined as information for action. The techniques of PDS were first developed as part of the global eradication of rinderpest. Participatory surveillance made significant contributions to the program by uncovering some of the last foci of disease. Since that time, PDS has been adopted by several official veterinary services around the world as a form of targeted surveillance in national control programs. Surveillance applications have included avian influenza, classical swine fever, peste des petits ruminants, Rift valley fever and foot and mouth disease (FMD). The flexibility, timeliness and sensitivity of PE and PDS can enhance the effectiveness of surveillance programs in both developed and developing country contexts. The capacity building process to establish PE relies on consultation between national stakeholders to conceptualize national epidemiological objectives, formulate the components of the comprehensive epidemiological program and then to build a training program to develop personnel with the key skills to put the research and surveillance plan in action. The Participatory Epidemiology Network for Animal and Public Health (PENAPH) has been established to help meet the demand for enhancement of epidemiological services. It does this through support for capacity building in PE, helping to capture lessons on good practice and to carrying out research to refine approaches for solving epidemiological problems. PENAPH takes an ecohealth approach and is built on a core partnership of seven complimentary organizations. These are the World Organisation for Animal Health, the UN Food and Agriculture Organization, the Interafrican Bureau for Animal Resources of the African Union, the International Livestock Research Institute, Veterinaires sans Frontieres – Belgium, Veterinarians without Borders/Veterinaires sans Frontieres – Canada and the Royal Veterinary College. The network is currently seeking partners in the public health field to further strengthen activities on the animal-human interface. In regard to FMD, participatory epidemiology has been used in original research, targeted assessments, economic evaluations and national surveillance programs. Documented studies include Catley et al (2004) who used participatory techniques to explore the association between chronic heat intolerance and FMD. In Pakistan, FMD was included as a target disease for the national PDS system (Mariner et al., 2001). The distribution and risk factors associated with persistence of FMD in Erzurum Province of Turkey have been documented (Admassu, 2005). Lastly, a participatory impact assessment leading to a cost benefit analysis for FMD control in a traditional livestock keeping community was completed in Western Upper Nile, southern Sudan (Barasa et al, 2005). These studies indicate that FMD is a disease readily recognized by livestock owners and that cattle keeping communities are an important source of knowledge to inform disease control strategies and analysis and targeting of health policies.
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 | 0,037 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,007 |
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 ».