Evidence map›Paper›PMID 42330167›Full record

ArticleJMIR public health and surveillance2026

Multimodal Data Approaches for Examining the 2024-2025 Highly Pathogenic Avian Influenza Outbreak in the United States: Descriptive Study.

Juliana Sopko, Aimee R Han, Jacqueline Powers, Jacquelin A Sauer, Mansi Avunoori, Stanislaw Zakrzewski, Allison Krugman, Abhishek Dasgupta, Kara Sewalk, Autumn Gertz and 7 more

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Juliana SopkoComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0009-0009-8080-0270
Aimee R HanComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0000-0001-8927-3432
Jacqueline PowersComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.
Jacquelin A SauerComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0000-0001-5694-8392
Mansi AvunooriComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0009-0003-8116-476X
Stanislaw ZakrzewskiTechnical University of Lodz, Lodz, Poland.
Allison KrugmanCouncil on Foreign Relations, New York, NY, United States.ORCID 0009-0002-1769-7417
Abhishek DasguptaDepartment of Biology, University of Oxford, Oxford, United Kingdom.
Kara SewalkComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0000-0002-2917-0869
Autumn GertzComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0000-0002-1642-8912
Benjamin RaderComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.
James SheldonInstitute for Experiential AI, Northeastern University, Boston, MA, United States.
Brennan KleinNetwork Science Institute, Northeastern University, Boston, MA, United States.
Jessica Malaty RiveraComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.
Moritz U G KraemerDepartment of Biology, University of Oxford, Oxford, United Kingdom.ORCID 0000-0001-8838-7147
Samuel V ScarpinoInstitute for Experiential AI, Northeastern University, Boston, MA, United States.
John S BrownsteinComputational Epidemiology Lab, Boston Children's Hospital, 401 Park Drive, 7th Floor West, Boston, MA, 02215, United States, 1 617 355 8278.ORCID 0000-0001-8568-5317

Funding

Rockefeller Foundation PC-2022-POP-005Wellcome Trust 228186/Z/23/Z
6 · The paper itself

Abstract

Background: Highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b, a globally predominant strain, was introduced into poultry in the United States in 2022 via spillover from wild birds, and has since been regularly reported, posing ongoing risks to animal and human health. In 2024, the United States reported the first known HPAI A(H5N1) clade 2.3.4.4b infection in dairy cattle, rapidly evolving into a multispecies outbreak among cattle and poultry, with spillover into humans. Publicly available data remained siloed and fragmented, hindering timely response. Innovative multimodal surveillance methods can enhance 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 A(H5) outbreak in the United States as an innovative, transparent, repeatable, and scalable approach for open-source public health surveillance. Methods: Global.health conducted real-time, multimodal surveillance of the United States 2024-2025 HPAI A(H5) outbreak using publicly available data for human cases (Centers for Disease Control and Prevention), animal outbreaks (United States Department of Agriculture), wastewater monitoring (WastewaterSCAN), genomic data (public genomic databases), research updates (scholarly communication), and policy changes and response measures (media and government) for the study period from February 1, 2024 through February 28, 2025. This digital data stream was used to create outbreak resources-an epidemiological linelist, event timeline, and interactive map-using a One Health framework to track emerging hotspots. Results: Global.health curated 70 confirmed human HPAI A(H5) cases across 13 states in a linelist, with exposure for nearly all (n = 65, 92.9%) cases associated with commercial agriculture and related operations. We curated 682 timeline entries across 6 distinct categories: human, cattle, response (eg, research, policy changes, and public health guidance), birds, genome, wastewater, and mammals. The map integrated human cases (n=70) and animal outbreaks (commercial cattle: n=977 and commercial poultry: n=325) into a single view. California was identified as the outbreak epicenter with high numbers of human cases (n=38, 54.3%), commercial cattle outbreaks (n=748, 76.6%), and commercial poultry outbreaks (n=66, 20.3%) during the study period. Wastewater surveillance detected the virus in California, with an unknown source at least 81 days before the first confirmed commercial dairy cattle case. Conclusions: Global.health's approach for integrating traditional and nontraditional public health surveillance data within a One Health framework enhanced early situational awareness during the United States 2024-2025 HPAI A(H5) outbreak, creating open access to resources that improve contextual understanding of the scope and evolution of this emerging zoonotic event. Further research should seek to understand the full potential of multimodal data in outbreak surveillance.

Indexed as

Disease OutbreaksInfluenza A Virus, H5N1 SubtypeInfluenza, HumanInfluenza in BirdsPopulation SurveillanceAnimalsBirdsCattleHumansPoultryUnited Statesavian influenzadata analysisdata curationdata visualizationdigital healthone healthpublic health surveillancewastewater surveillancezoonotic infectious diseaseszoonotic spillover

Identifiers

PMID42330167
PMCPMC13286079

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.