JMIR Publications

ABSTRACT

Background:

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.

Objective:

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.

Methods:

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

Results:

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.

Conclusions:

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.