Reported Global Avian Influenza Detections Among Humans and Animals During 2013-2022: Comprehensive Review and Analysis of Available Surveillance Data
Notice bibliographique
Résumé
BACKGROUND: Avian influenza (AI) virus detections occurred frequently in 2022 and continue to pose a health, economic, and food security risk. The most recent global analysis of official reports of animal outbreaks and human infections with all reportable AI viruses was published almost a decade ago. Increased or renewed reports of AI viruses, especially high pathogenicity H5N8 and H5N1 in birds and H5N1, H5N8, and H5N6 in humans globally, have established the need for a comprehensive review of current global AI virus surveillance data to assess the pandemic risk of AI viruses. OBJECTIVE: This study aims to provide an analysis of global AI animal outbreak and human case surveillance information from the last decade by describing the circulating virus subtypes, regions and temporal trends in reporting, and country characteristics associated with AI virus outbreak reporting in animals; surveillance and reporting gaps for animals and humans are identified. METHODS: We analyzed AI virus infection reports among animals and humans submitted to animal and public health authorities from January 2013 to June 2022 and compared them with reports from January 2005 to December 2012. A multivariable regression analysis was used to evaluate associations between variables of interest and reported AI virus animal outbreaks. RESULTS: From 2013 to 2022, 52.2% (95/182) of World Organisation for Animal Health (WOAH) Member Countries identified 34 AI virus subtypes during 21,249 outbreaks. The most frequently reported subtypes were high pathogenicity AI H5N1 (10,079/21,249, 47.43%) and H5N8 (6722/21,249, 31.63%). A total of 10 high pathogenicity AI and 6 low pathogenicity AI virus subtypes were reported to the WOAH for the first time during 2013-2022. AI outbreaks in animals occurred in 26 more Member Countries than reported in the previous 8 years. Decreasing World Bank income classification was significantly associated with decreases in reported AI outbreaks (P<.001-.02). Between January 2013 and June 2022, 17/194 (8.8%) World Health Organization (WHO) Member States reported 2000 human AI virus infections of 10 virus subtypes. H7N9 (1568/2000, 78.40%) and H5N1 (254/2000, 12.70%) viruses accounted for the most human infections. As many as 8 of these 17 Member States did not report a human case prior to 2013. Of 1953 human cases with available information, 74.81% (n=1461) had a known animal exposure before onset of illness. The median time from illness onset to the notification posted on the WHO event information site was 15 days (IQR 9-30 days; mean 24 days). Seasonality patterns of animal outbreaks and human infections with AI viruses were very similar, occurred year-round, and peaked during November through May. CONCLUSIONS: Our analysis suggests that AI outbreaks are more frequently reported and geographically widespread than in the past. Global surveillance gaps include inconsistent reporting from all regions and human infection reporting delays. Continued monitoring for AI virus outbreaks in animals and human infections with AI viruses is crucial for pandemic preparedness.
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 | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».