Multimodal Data Approaches for Examining the 2024-2025 Highly Pathogenic Avian Influenza Outbreak in the United States: Descriptive Study
Notice bibliographique
Résumé
<sec> <title>BACKGROUND</title> Highly pathogenic avian influenza (HPAI) outbreaks have primarily affected wild and domesticated bird populations, with occasional human spillover. In 2024, the United States (US) reported the first known HPAI H5N1 infection in dairy cattle, which rapidly evolved into a multispecies outbreak among cattle and poultry with spillover into humans. Publicly available data remained siloed and fragmented across agencies, which has implications for timely response. Innovative multimodal surveillance methods present an opportunity to enhance early situational awareness through comprehensive, standardized data collection, integration, and visualization. </sec> <sec> <title>OBJECTIVE</title> This study aimed to describe observations from the application of enhanced surveillance methods that collect, integrate, and visualize multimodal data for real-time tracking of the 2024-2025 HPAI outbreak in the US as an innovative, transparent, repeatable, and scalable approach for open source public health surveillance for zoonotic or other emerging or re-emerging pathogens. </sec> <sec> <title>METHODS</title> The Global.health consortium conducted real-time, multimodal data collection on the US HPAI outbreak between February 1, 2024 and February 28, 2025 using publicly available data for human cases, animal outbreaks, wastewater surveillance, genomic data, research updates, policy actions, and response measures. This digital data stream of traditional and non-traditional sources was used to create outbreak resources—a line-list, event timeline, and interactive map—using a One Health framework to track emerging hotspots </sec> <sec> <title>RESULTS</title> Seventy human HPAI cases were confirmed across 13 US states, with exposure for nearly all (92.9%) cases associated with commercial agriculture and related operations. Only one human case of HPAI had ever been documented in the US prior to 2024, underscoring a sharp rise in incidence. We curated 682 Timeline entries across six distinct categories: human, cattle, response, birds, genome, wastewater, and mammals. California was identified as the outbreak epicenter with leading numbers in human cases (n= 38, 54.3%), cattle (n=748, 76.6%), and poultry infections (n=66, 20.3%) during the study period. Wastewater surveillance provided an early warning sign, identifying viral presence in California at least 81 days before the first dairy cattle case. </sec> <sec> <title>CONCLUSIONS</title> The integration of traditional and non-traditional public health surveillance data into a single view within a One Health framework improved contextual understanding and enhanced situational awareness during the 2024-2025 HPAI outbreak in the US. Wastewater detections identified early viral presence, marking a critical window for intervention, policy action, and response to curb spread. Access to an open source multimodal data platform - like that put forward by Global.health - in real-time can assist researchers, public health officials, and decision makers in understanding the origins, scope, and evolution of emerging zoonotic diseases that fragmented, more traditional surveillance systems may be unable to readily provide. Further research should be conducted to understand the full potential of multimodal data in real-time outbreak surveillance. </sec>
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Comment cette classification a été obtenuedéplier
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 | 0,010 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».