A Systematic Review of New, Enhanced Surveillance Systems and Methodologies for Zoonotic Influenza Viruses in Animals and Human–Animal Interface

TL;DR

This paper reviews recent advances in surveillance methods for zoonotic influenza viruses between 2017 and 2024, highlighting gaps and suggesting ways to improve global monitoring.

Contribution

The study provides a systematic review of enhanced surveillance methodologies for zoonotic influenza, identifying key achievements and unmet needs since the 2017 WHO agenda.

Findings

Most countries lack active surveillance programs for zoonotic influenza at the human–animal interface.

Novel diagnostic technologies and noninvasive surveillance approaches need wider adoption and validation.

Stronger integration of surveillance data with risk assessment and policy is recommended.

Abstract

In 2009, the World Health Organization (WHO) developed a public health research agenda for influenza to guide researchers and outline directions and priority areas for research on influenza aiming at reducing the burden of seasonal epidemic influenza and the risk and impact of pandemic influenza. The agenda was updated in 2017, but since then, important research has been conducted, and major changes have occurred to the global health landscape impacted mainly by the COVID‐19 pandemic. Therefore, there is a need to assess advances in zoonotic influenza surveillance methods reported between 2017 and 2024 in order to highlight key achievements and identify remaining gaps that limit their broader implementation, hence informing an update of the research agenda. We conducted a comprehensive literature review of zoonotic influenza surveillance and monitoring, focusing on novel and enhanced methodologies reported globally between 2017 and 2024. A systematic analysis was performed following PRISMA guidelines on 7490 peer‐reviewed manuscripts from 2017 to 2024 retrieved from PubMed, of which 164 records were included in this review. Analysis of the information collected indicated several advances and gaps at different levels of surveillance and unmet public health needs. Most countries do not have active and comprehensive surveillance programs for zoonotic influenza at the human–animal interface, which underestimates the true burden of zoonotic influenza diseases. The review concludes with a set of high‐priority research recommendations focused on filling gaps in One Health data integration, validation, and field deployment of novel diagnostic technologies, wider adoption of noninvasive and environmental surveillance approaches, and stronger linkage of methodological innovations to risk assessment and policy action. In light of the recent upsurge in H5N1 activity and cross‐species transmission, the WHO has convened multiple R&D Blueprint consultations over the past year to prioritize research and development for H5N1 candidate vaccines, diagnostics, and pandemic preparedness. These ongoing initiatives underscore the critical importance of strengthening surveillance at the human–animal interface.

Figures

PRISMA flow diagram of the screening and selection of studies for this review.

Tables

TABLE 1. Novel environmental sampling methods developed globally from 2017 to 2024.

Environmental sample	Sampling technique	Aim	Uses for surveillance
Water	Combining remote sensing, filtration and sequencing [46]	Improving environmental surveillance	Novel and effective approach for AIV monitoring
Water	Combining a hydrodynamic model with data on mallard abundance and AIV infection rate [47]	Creating AIV transmission risk maps	Efficient for improved surveillance
Water	1 L and 50 L water sampling [48]	Detection of AIV in water	Detection of AIV in wetlands
Bioaerosol	Swine oral fluids, surface swabs, and air sampling [26]	Detection of influenza virus in swine	Effective noninvasive approach for detecting influenza virus among swine
Bioaerosol	Air sampling [27]	Detection of swine pathogens	Effective noninvasive approach for detecting pathogens in animals
Dust	Dry wipes rubbed against the walls or feeders [28]	Viral surveillance with the addition of BSA to eliminate inhibitors	Effective method for HPAI monitoring
Feces	Fresh feces sampling (droppings) [30]	Pathogen surveillance in wild birds	Cost‐effective, rapid, and noninvasive method that can be applied under sub‐Saharan African conditions
Feathers	Contour feather sampling [49]	AIV surveillance in birds	Effective noninvasive method for the accurate detection of AIV in commercial poultry

TABLE 2. Multiplex PCR methods developed from 2017 to 2024 for the detection of influenza viruses.

Method	Type detected	Study country
Multiplex RT‐qPCR [66]	Detection of AIVs subtypes H5, H7, and H9	China
Tetraplex RT‐qPCR [67]	Simultaneous screening of four influenza virus types, A, B, C and D in swine	Germany
Triplex RT‐qPCR [68]	Detection of duck‐origin AIVs, Newcastle disease virus (NDV), and duck Tembusu virus (DTMUV)	China
Triplex RT‐qPCR [69]	Simultaneous detection of AIVs subtypes H3, H4, and H5	China
Triplex RT‐qPCR [70]	Simultaneous detection of AIVs subtypes H5, H7, and H9	China
Multiplex RT‐qPCR assay using TaqMan minor groove binder (MGB) probes [71]	Detection of H10 subtype AIVs	China
Multiplex RT‐qPCR [72]	Simultaneously detection of pan‐H5 HPAI viruses and H5Nx clades 2.3.2.1 and 2.3.4.4 viruses	Republic of Korea
Quadruple RT‐qPCR [73]	Simultaneous detection of H7N9 and identification of HP and NAI‐resistance mutations	China
Double RT‐qPCR [74]	Detection of H5N6 influenza virus	China
Multiplex one‐step RT‐qPCR [75]	Detection of nine AIVs NA subtype	China
Multiplex RT‐qPCR [76]	Detection of H6 AIVs	China
RT‐PCR assays using newly designed NS and PB2 primers and probes [77]	Detection of IAVs	China
TaqMan probe chemistry (TaqMan multitarget) [78]	Simultaneous detection of AIVs (M gene) and subtyping (H5, N1, H9, N2)	Bangladesh
RT‐qPCR [79]	Detection of two H9 lineages of AIVs	Japan
RT‐qPCR [80]	Detection of the novel H5N6 virus	China
Real‐time RT‐PCR [81]	Detection of Cluster IV H3N2v	Japan
SYBR(R) Green‐based RT‐qPCR [82]	Subtyping of avian influenza HA	US
Multiplex RT‐PCR [83]	Detection and subtyping of IAVs	Brazil
Multiplex RT‐qPCR [84]	Detection of H5 AIVs	China
GeXP multiplex RT‐PCR [85]	Detection of eight AIVs subtypes (H1, H2, H3, H5, H6, H7, H9 and H10)	China
Multiplex RT‐qPCR [86]	Differentiation of the NA and HA genes of the three major IAVs subtypes	Brazil
qPCR assays [87]	Targeting of HA and NA gene lineages relevant for swIAV and detection of internal genes of IAV	Denmark
Pan‐AIV RT‐qPCR [88]	Detection of all AIVs	China
H5‐AIV RT‐qPCR [88]	Detection of H5 AIVs	China
Pan‐H9 RT‐qPCR [89]	Detection H9Nx viruses of any of the Y439, Y280, and G1 clades	Italy
RT‐qPCRs [90]	Subtyping of European swine IAVs	France
Multianalyte suspension assay (MASA) combining one‐tube multiplex RT‐qPCR with bead hybridization and detection [91]	Subtyping of AIVs	China
Real‐time reverse transcription recombinase‐aided amplification (rRT‐RAA) [92]	Detection of IAVs	China
RT‐RPA/CRISPR [93]	Detection of AIVs	China
Multiplex asymmetric RT‐PCR‐electrochemical DNA [94]	Simultaneous detection and subtyping of IAVs	China
xTAG‐multiplex PCR array [80]	Detection of avian influenza virus, Newcastle disease virus, infectious bronchitis virus, and infectious laryngotracheitis virus	China
Multiplex PCR and Matrix‐assisted laser desorption/ionization—time‐of‐flight mass spectrometry (MALDI‐TOF) [96]	Simultaneous detection and genotyping of 10 viruses in ducks including AIVs [96]	China

TABLE 3. ELISA methods developed from 2017 to 2024 for the detection of influenza viruses.

ELISA method	Type detected	Application
Sandwich ELISA [131]	HA of avian influenza A (H10N8)	Diagnostic of H10N8
AC‐ELISA and ICT [132]	H9N2 AIV	Field investigation of H9N2 subtype
DAS‐ELISA [133]	H9 viral antigen	H9 AIV diagnostic method
Sandwich ELISA [134]	H3 AIV	Detection of H3 AIV
Sandwich ELISA [134]	H4 AIV	Detection of H4 AIV
Sandwich ELISA [135]	NA of H7N9 AIV	Detection of H7N9 virus and quantification of N9 protein
ELASA [136]	Influenza A	Detection of Influenza A in analysis laboratories
AC‐ELISA [137]	HA of H6 AIV isolate	Diagnosis of H6 AIV infections
mAb sandwich ELISA [138]	Infectious diseases	Detection of infectious diseases
AC‐ELISA [139]	H7N9 AIV	Detection of H7N9
Ag‐ELISA [132]	H5 AIV	Detection of H5 AIV and field investigation of avian A(H5) viruses
Double‐antibody sandwich ELISA [133]	H7 AIV and quantification of H7 protein	Diagnosis and antigen quantification of H7 subtype
Antigen‐capture ELISA [140]	HA of H5 viruses	Rapid diagnosis of H5 virus infections

Full text

Introduction

1

Zoonotic influenza viruses pose a major public health concern due to their capacity to cross the species barrier and infect humans, often with devastating consequences [1]. The 2009 pandemic due to A(H1N1)pdm09, showed how zoonotic viruses can rapidly adapt to infect humans with a virus likely originating from swine and harboring genetic components from human, swine, and avian influenza A viruses [2]. Within months of its initial detection on a pig farm in Mexico, the virus spread globally, resulting in millions of infections and substantial mortality, particularly among vulnerable populations such as young children and those with underlying health conditions [3]. The rapid transmission of A(H1N1)pdm09 was facilitated by several factors, including the interconnectedness of global travel and trade, which allowed the virus to transmit across borders and continents with alarming speed [4]. Public health systems worldwide were overwhelmed and struggled to keep pace with the outbreak's dynamics [5] highlighting weaknesses in existing surveillance programs and the need for robust surveillance systems that can effectively monitor and detect influenza in both wildlife and domestic animals that could serve as reservoirs for emerging pathogens. Moreover, the A(H1N1)pdm09 experience highlighted the importance of interspecies transmission pathways, as the virus was linked to pigs and other animals before it adapted to human hosts [6]. This emphasized the need for a One Health approach that recognizes the interconnectedness of human, animal, and environmental health. The pandemic underscored the critical role of surveillance systems in monitoring human health outcomes but also in tracking influenza activity in animal populations, enabling early detection of potential spillover events. The complex ecology of influenza viruses in wildlife and domestic animals further complicates detection efforts [7]. In recent years, the emergence of novel influenza strains, driven by factors such as climate change, habitat destruction, and increased human–animal interactions has facilitated the exchange of pathogens and increased the risk of zoonotic transmission [8]. The recent outbreaks of highly pathogenic avian influenza (HPAI) H5N1 in cattle and goats in the United States of America (US), the detection of the virus in deceased cats infected likely through unpasteurized milk from infected cows, and reported infections in farm workers raise serious global health concerns, highlighting the virus's expanding host range and zoonotic potential [9, 10, 11]. This dynamic underscores the necessity for comprehensive surveillance strategies that can monitor these interactions in real‐time.

To enhance global knowledge and response to influenza, the World Health Organization (WHO)’s Global Influenza Programme developed the WHO public health research agenda for influenza in 2009. This agenda identifies research priorities for pandemic, zoonotic, and seasonal influenza, emphasizing a multidisciplinary approach and collaboration among researchers, public health officials, and policymakers. It was reviewed in 2010–2011 and 2017 to guide research for the next 5–10 years. Since 2017, significant research and changes, especially due to COVID‐19, have occurred, necessitating an assessment of knowledge and data gaps from 2017 to 2024. The unprecedented spread of HPAI H5N1 among wild birds, domestic animals, cattle and recent human cases have raised serious global health concerns. In response, WHO convened an R&D Blueprint consultation to prioritize containment and mitigation measures of pandemic H5N1 influenza [12]. Integrating such global R&D efforts with improved field surveillance will be crucial for pandemic preparedness.

In this study, we conducted a comprehensive literature review to systematically assess novel and enhanced surveillance systems and methods for zoonotic influenza viruses published between 2017 and 2024. This focus directly responds to the 2017 WHO Public Health Research Agenda for Influenza, which prioritized improvements in surveillance tools and approaches at the human–animal interface. We do not aim to cover all aspects of zoonotic influenza research in this review; instead, we highlight advances in surveillance systems and methodologies, summarize their reported effectiveness (including sensitivity, specificity and timeliness), and identify remaining limitations and gaps that affect the broader status of zoonotic influenza surveillance. By aligning recent innovations with the WHO research priorities, this review provides a clearer picture of progress made and areas still requiring attention. The review focuses on enhanced and novel reported surveillance systems that are in place for outbreaks of influenza A viruses (IAVs) in wild and domestic animals highlighting application of surveillance in wild animals, the environment, domestic animals, and exposed humans. It also highlights novel methodologies and approaches and assesses their effectiveness in terms of reported performance metrics such as sensitivity, specificity, timeliness, feasibility for field deployment, and contribution to improved risk assessment of zoonotic IAVs.

Methods

2

Search Strategy

2.1

This study was designed following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) 2020 for review of the published peer‐reviewed literature. This study did not require institutional review board approval. We used PubMed to search for English‐language peer‐reviewed publications using the search terms “avian influenza,” “swine influenza,” and “zoonotic influenza viruses” from 2017 till 2024 (Figure 1). In total, 7490 records were exported to an Endnote X8 (Endnote, Berkley, CA) library (Figure 1). Duplicates and papers published after 31 March 2024 were removed, yielding 6143 publications retained and exported to a master Excel spreadsheet (Microsoft, Redmond, WA). The following data were extracted from each included study: publication year, country, and category. We then searched for the keyword “surveillance” in the master Excel spreadsheet to select the papers that discussed surveillance. The search yielded 1179 manuscripts. Those records were exported to a new Excel sheet. Titles and abstracts were reviewed thoroughly and were independently screened by two researchers to select the papers discussing new and enhanced methodologies and surveillance systems for detection of zoonotic influenza viruses. Additional records from the initial library (6143), which did not include the word “surveillance” but showed to be relevant, were re‐included to ensure that all relevant publications were captured. A total of 164 research papers were included in this review. The selection was confirmed by a senior scientist with extensive expertise in public health and more than 10 years of experience in zoonotic influenza.

PRISMA flow diagram of the screening and selection of studies for this review.

Eligibility Criteria

2.2

The included publications met the following eligibility criteria: (1) publications reporting new or enhanced methodologies or surveillance systems for detection of zoonotic influenza viruses; (2) surveillance conducted in at least one of the following contexts: domestic animal husbandry (farmed poultry, swine, cattle, goats, and related production systems, including live bird markets and smallholder farms), wild animals (e.g., wild birds or other wildlife reservoirs), environmental samples (e.g., air, water, wastewater, soil), or exposed humans at the human–animal interface; (3) publications are published in the period from 2017 to the end of March 2024. Publications were excluded for the following reasons: (1) duplicates, (2) published after March 31, 2024, (3) records not reporting new or enhanced surveillance systems for detection of zoonotic influenza viruses.

Data Collection Process

2.3

We collected data from the 164 retained peer‐reviewed publications related to enhanced and novel surveillance systems for zoonotic influenza virus applied in domestic animal husbandry (farmed poultry, swine, cattle, goats, and related production systems) and exposed humans, enhanced and novel surveillance systems applied in animals in the wild, and enhanced and novel surveillance systems applied to the environment (air, water, wastewater). We also extracted information related to novel methodologies and approaches for detection and forecasting. Additionally, we extracted any information related to assessing and studying the effectiveness of the enhanced or novel surveillance systems in improving risk assessment on human infection by animal IAVs.

Results

3

Study Selection

3.1

A total of 1179 records whose title and/or abstract contain the keyword “surveillance” were retained from PubMed and were thoroughly screened to extract information related to new and enhanced methodologies and surveillance systems for detection of zoonotic influenza viruses. A total of 164 peer‐reviewed papers were included in this review (Figure 1). Most studies originated from East and Southeast Asia (e.g., China, Japan, Republic of Korea, Thailand), Europe (e.g., Italy, United Kingdom (UK), Netherlands, France, Germany, Denmark), and North America (primarily US and Canada). Fewer studies were conducted in Africa (e.g., South Africa), South America (e.g., Brazil, Argentina), and the Middle East (e.g., Iran).

Findings

3.2

Since 2017, several advances in developing novel influenza surveillance systems or enhancing existing surveillance programs to improve monitoring of influenza viruses and enhance early detection and response were reported. Livestock and poultry are susceptible to IAVs and should be monitored closely when outbreaks in wildlife are detected and through routine surveillance. The application of a One Health approach is crucial to detect emerging influenza zoonotic viruses that can pose a threat to exposed humans.

Application in Domestic Animal Husbandry and Exposed Humans

3.2.1

Predictive and Risk‐Based Surveillance Systems

3.2.1.1

Over the past 8 years, predictive surveillance systems have been developed to assess the risk of zoonotic influenza transmission from wild to domestic animals using influenza transmission, demographic, and spatial risk factors. The RealOpt‐ASSURE system effectively integrates compartmental modelling with human behavior data for monitoring avian influenza virus (AIVs) outbreaks [13]. Research has shown that analyzing the density of avian influenza‐infected dabbling ducks in Northern Italy could be used to identify the risk of introduction of AIVs into poultry flocks [14]. Avian influenza‐driven risk maps have been created to evaluate poultry and wild bird densities in England for targeted risk‐based sampling strategies [15]. Studies also link wild bird densities to the risk of HPAI infections in poultry in the Netherlands [16]. The Random Forest model has been identified as the most effective predictive model for estimating IAVs frequency in swine in Ontario, Canada [17]. A machine learning framework has been proposed for rapid HPAI risk prediction, considering environmental and meteorological data, farm biosecurity measures, and wild bird HPAI surveillance data [18]. A predictive framework utilizing an extensive spatiotemporal dataset has been developed to understand AIVs drivers and forecast future outbreaks in Korea [19].

Community‐Based and Digital Surveillance Approaches

3.2.1.2

In Kenya, mobile phone syndromic surveillance has been useful for detecting severe diseases but may not catch early avian influenza outbreaks [20]. A participatory One Health disease detection system was developed in Thailand as a novel community‐owned disease surveillance system that allows community members to monitor health issues, facilitating early disease detection [21]. An integrated behavioral biological surveillance approach that targets high‐risk populations where humans, livestock, and wildlife interact was applied in Thailand, helping policymakers enhance surveillance strategies and behavior‐focused interventions [22]. Additionally, a study in the UK analyzed posts on the social media platform Twitter (now X) and found only a weak correlation between avian influenza–related messages and confirmed cases, suggesting that social media data alone may have limited reliability for surveillance but could provide supplementary insights when combined with traditional systems [23].

Enhanced Sampling Strategies

3.2.1.3

Enhanced sampling approaches were reported from 2017 to 2024. A study comparing individual, group, and environmental sampling methods for influenza surveillance in pigs in the US found that group and environmental sampling were more effective for detection, while individual sampling is better for viral isolation and sequencing [24]. Udder skin wipes from lactating sows were identified as a cost‐effective and sensitive method for detecting and isolating IAVs in the US, with pooling up to three wipes not affecting sensitivity [25]. In swine farm settings, two studies evaluated bioaerosol and surface sampling as early detection tools, emphasizing the importance of sampling environment design on detection outcomes [26, 27]. Another study further demonstrated that using additives such as bovine serum albumin (BSA) in environmental dust samples can significantly improve real‐time quantitative reverse‐transcriptase polymerase chain reaction (RT‐qPCR) sensitivity [28]. Although few studies explicitly address the use of sentinel birds in large poultry units, targeted wild bird surveillance approaches suggest parallels that could inform sentinel‐based strategies [29, 30]. Monitoring urban rats was effective for studying zoonotic viruses in Vienna, Austria [31]. Another study showed that combining active surveillance in commercial game birds with mortality‐based triggers improved HPAI detection rates [32]. Duck hunting preserves in Maryland, US were identified as a useful model for studying AIV dynamics among nonmigratory birds [33]. A study showed the feasibility of asymptomatic avian influenza surveillance in exposed persons in England as an approach for avian influenza detection [34]. Lastly, innovative methods, including sediment sampling from wetlands in Canada [35] and a virion enrichment technique developed in Japan (MiVET) [36], improved detection of AIVs in various environments.

Integrated One Health Surveillance Systems

3.2.1.4

Thailand piloted a One Health avian influenza surveillance system in 2016, integrating human, animal, and environmental stakeholders to monitor influenza A viruses, which involves triggering public health measures upon suspected cases, coordinating sample collection and testing, and sharing results while highlighting both strengths in simplicity and flexibility and challenges in data quality and interoperability [37].

Data Visualization and Analytical Tools

3.2.1.5

Tools for surveillance data visualization were developed to improve access to and interpretation of influenza surveillance data. The Iowa State University veterinary diagnostic laboratory developed a website for visualizing real‐time swine IAV data and linking it to the United States Department of Agriculture (USDA) surveillance system [38]. In addition, OctoFLUshow, an interactive platform, was developed to analyze and report IAV strains collected since 2009. A complementary study presented a systematic approach for analyzing USDA swine IAV surveillance sequencing data, integrated with OctoFLUshow to provide a searchable overview of strain diversity in the US [39].

Application in Animals From the Wild

3.2.2

Surveillance efforts for AIVs have begun to expand beyond traditional domestic and avian hosts to include less commonly monitored species. A study explored urban zoonotic virus surveillance using city rats, and while not focused on canids, the study highlights the potential of opportunistic surveillance in urban wildlife [31]. Predictive systems for surveillance of avian influenza in the wild were developed. A gradient boosted trees method enhanced active surveillance for AIVs in the US using geographic location and RT‐qPCR results as predictors [40]. The Observation.org platform effectively monitored wildlife pathogens by correlating observations of sick and dead birds with outbreaks reported by the World Organisation for Animal Health (WOAH), aiding early detection of HPAI outbreaks in Belgium and the Netherlands [41]. Additionally, the Citizen Scientist eBird database helped map wild bird distributions in Taiwan, informing cost‐effective AIV surveillance site selections [42]. A spatiotemporal model based on habitat suitability and avian influenza detection coordinates was developed to support a risk‐based surveillance system for HPAI prevention and preparedness [43]. Three active sampling methods showed that hunter‐sourced waterfowl sampling in the UK could detect avian influenza before the occurrence of outbreaks on poultry, allowing for early intervention to protect national poultry [29]. A new method for sequencing hemagglutinin and neuraminidase directly from wild bird faucal RNA using Nanopore Flongle proved to be cost‐effective and easier than traditional methods [44]. Additionally, a “fireworks” spatiotemporal model was developed to simulate and visualize the spread of HPAI cases over time and across geographic areas, named for the way outbreaks radiate outward like fireworks, thereby supporting the improvement of surveillance systems [45].

Application to the Environment

3.2.3

From 2017 to 2024, novel environmental sampling methods have been developed for avian influenza detection and are summarized in Table 1. An approach has been designed to improve the detection of AIV in California wetlands utilizing remote sensing, filtration, and sequencing [46]. Bioaerosol and surface sampling showed effectiveness in swine surveillance in Canada, complemented by a mobile data collection application [26]. A noninvasive bioaerosol surveillance strategy was tested for detecting swine pathogens in North Carolina swine farms [27]. The addition of BSA enhanced RT‐qPCR sensitivity for HPAI virus detection in dust samples from French poultry farms affected by A(H5N1) outbreaks [28]. A study conducted in South Africa showed that fresh faucal samples collected immediately have detection rates similar to or higher than oropharyngeal and cloacal swabs [30]. Other methods included using feather samples for noninvasive detection in commercial poultry in the US [49] and combining hydrodynamic models with mallard abundance data to identify AIV hotspots in Southern France [47]. A spatial model was developed in Argentina to identify high‐risk areas for low pathogenic avian influenza virus occurrence at the backyard poultry‐wild bird interface using expert opinions and ecological niche modelling to inform surveillance [50]. A spatial framework assessed regions at high risk for influenza virus spillover by quantifying the geographic variation in outbreak emergence potential [51]. Additionally, two water sampling methods were developed in the Netherlands for AIV detection, with the 1 L method proving quicker and easier to process compared to the 50 L method, both effective for environmental screening [48]. A review suggested that environmental sampling may not be a reliable detection method especially when the infection and contamination are less prevalent, and thus information about the species and source of the virus might be missed [52].

Novel Methodologies and Approaches for Detection and Forecasting

3.2.4

Conventional surveillance methods have been improved by the adoption of real‐time PCR and the upgrading and designing of new methods and assays for the rapid detection and characterization of influenza viruses. Furthermore, advancements in next‐generation sequencing technology and the emergence of genomic surveillance approaches allowed the genetic analysis of viral strains and understanding of their origin and spillover potential.

Nucleic Acid Amplification–Based Methods

3.2.4.1

New reported nucleic acid amplification methods for detecting AIVs included a rapid convection PCR (cPCR) platform for A(H5) and A(H9) subtypes [53], and an isothermal reverse transcription recombinase polymerase amplification with lateral‐flow dipstick (RT‐RPA‐LFD) assay that is 10 times more sensitive than conventional RT‐PCR for A(H9) detection [54]. A lateral flow dipstick recombinase polymerase amplification (LFD‐RPA) assay achieved 100% sensitivity and specificity for A(H7N9) without cross‐reactivity [55]. Additional methods like real‐time fluorescence and reverse transcription recombinase‐aided amplification (RF‐RT‐RAA), reverse transcription recombinase‐aided amplification combined with lateral flow dipstick (RT‐RAA‐LFD) [56, 57], and a CRISPR‐Cas13a based platform were developed for rapid detection of various avian influenza subtypes [58]. Real‐time PCR methods including four singleplex RT‐qPCR assays were developed for the rapid and simultaneous detection of avian influenza subtypes A(H4), A(H6), and A(H10), suitable for laboratory and clinical use [59]. A USDA‐validated RT‐qPCR targeting the M gene effectively detects most influenza A viruses and adapts to new strains [60]. Additional assays include a real‐time probe‐based assay for the rapid and specific detection of endemic A(H9N2) AIV [61], a TaqMan RT‐qPCR for A(H2) [62], a sensitive Uni Kor‐H9 assay for various A(H9Nx) viruses [63], a TaqMan RT‐qPCR for A(H5) [64], and a cost‐effective highly pathogenic A(H5) RT‐qPCR for clade 2.3.4.4b A(H5) virus detection [65]. Novel multiplex PCR methods [66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96] developed from 2017 to 2024 are summarized in Table 2.

Insulated isothermal reverse transcriptase PCR methods were also developed for timely pathogen detection at sampling sites, such as insulated isothermal RT‐PCR for H7N9 [97, 98] with high sensitivity and specificity, and two insulated isothermal PCR devices, POCKIT DUO and POCKIT Central, and showed comparable performance to real‐time reverse transcription PCR for avian influenza detection [99].

Researchers developed reverse transcription loop‐mediated isothermal amplification (RT‐LAMP) diagnostic assays to detect a variety of zoonotic influenza viruses. A rapid and sensitive reverse RT‐LAMP assay was developed for detecting swine influenza virus in nasal samples, distinguishing between A(H1) and A(H3) strains directly from field samples without RNA extraction [100]. Three RT‐LAMP assays targeting the universal Matrix (M) gene, A(H5), and A(H9) HA for avian influenza showed higher sensitivity and faster amplification than conventional RT‐PCR [101]. One study described the application of RT‐LAMP to detect the M gene of IAVs in swine and showed very high sensitivity and specificity [102]. One study reported the rapid detection of seasonal and avian influenza viruses using RT‐LAMP technology with a one‐pot colorimetric visualization system, providing results within an hour [103]. A novel real‐time colorimetric RT‐LAMP assay was developed for the cost‐effective and rapid detection of avian influenza clade 2.3.4.4b H5 [104]. A highly efficient LAMP primer was developed for a real‐time RT‐LAMP assay that detects nine avian influenza subtypes and enables on‐site diagnosis without RNA extraction [105]. An integrated centrifugal RT‐LAMP disc was developed for the rapid detection of influenza A subtypes H1, H3, H5, H7, H9, and influenza B in clinical samples [106]. While nucleic amplification methods offer high sensitivity and specificity, they generally require trained personnel, controlled laboratory environments, and cold‐chain reagents, which may limit their application in low‐resource or field settings.

Optical‐Based Methods

3.2.4.2

Surface‐enhanced Raman scattering–based assays have also shown applicability for the development of robust zoonotic influenza diagnostic tools [107, 108, 109]. Many immunochromatographic detection kits were developed for rapid and field detection of different subtypes of influenza viruses using novel monoclonal antibodies including H7N9 and H5Nx, highlighting their potential for effective surveillance and diagnosis in poultry and clinical settings [110, 111, 112, 113, 114, 115, 116, 117]. Few publications described fluorescent diagnostic assays that could detect simultaneously multiple influenza viruses, utilizing technologies like DNA‐templated silver nanoclusters [118], multiplex immunofluorescence platform based on ZnO nanorods [119], peptide‐linked systems to identify multiple virus subtypes efficiently [120], and label‐free imaging array capable of detecting simultaneously traces of three subtypes of AIVs DNA biomarkers (H1N1, H7N9, and H5N1) [121]. While optical‐based assays provide rapid and multiplexed detection, they often rely on specialized equipment, fluorescence labelling, or nanostructures, which can increase costs and reduce portability for field deployment.

Nanoparticle‐Based Methods

3.2.4.3

Nanoparticle‐based methods detect influenza viruses by exploiting the unique optical, magnetic, or binding properties of nanoparticles, which change when they interact with viral proteins or nucleic acids [122]. These properties can then be measured using color change, fluorescence, or signal amplification, enabling rapid virus detection. Nanoparticle‐based methods were developed for the rapid detection of influenza viruses circulating in humans and animals that could be used in field surveillance [123, 124], including cell‐mimetic nanoparticles [125], cysteamine‐gold coated carboxylated europium chelated nanoparticle‐mediated dual‐mode point‐of‐care testing [126], glycan‐functionalized gold nanoparticles [127], combination peptide nucleic acid and unmodified gold nanoparticles [128], lateral flow immunoassays [129], and integrated magneto‐opto‐fluidic platform [130]. Nanoparticle‐based assays show promise for rapid and sensitive testing, yet they may require sophisticated instruments.

Enzyme‐Linked Immunosorbent Assay–Based Methods

3.2.4.4

Various novel enzyme‐linked immunosorbent assay (ELISA) methods were developed from 2017 to 2024 for the rapid, sensitive, and specific detection of different avian influenza virus subtypes, utilizing monoclonal antibodies and innovative techniques to enhance diagnostic capabilities in laboratory and field settings [131, 132, 133, 134, 135, 136, 137, 138, 139, 140]. Those methods are summarized in Table 3. While traditional ELISA platforms are limited by cross‐reactivity, antigenic drift, and the need for monoclonal or polyclonal antibodies, the reviewed studies tried to overcome these obstacles by employing subtype‐specific antibodies, novel antigen‐capture formats, and innovative detection systems. Nevertheless, ELISA assays require ongoing optimization, particularly for field applications.

Chip‐ and Probe‐Based Methods

3.2.4.5

Chip‐based methods were used to develop diagnostic kits with improved detection for use in biological warfare studies and for application on unpurified field samples [141, 142]. Probe‐based methods were developed to detect and amplify influenza viral sequences with high specificity and sensitivity even with degraded or low amount of viral RNA. Those methods include chemiluminescent lateral flow immunoassays that outperform commercial kits [143], universal influenza enrichment probes capable of detecting degraded viral RNA [144], as well as a fluorescent dye for rapid detection of avian influenza viruses by fluorescent immunochromatographic test [145]. A nucleic acid probe‐based electrochemical biosensor was developed to detect influenza A virus with enhanced sensitivity and selectivity [146]. Additionally, a hybridization capture probe panel was developed for effective avian influenza virus surveillance and subtyping [147]. A strand‐specific hybridization assay was developed capable of differentiating actual IAVs infection from environmental contamination [148]. A dual‐modality immunoassay to detect H9N2 AIV was developed based on integrating electrochemistry with fluorescence in one analytical system and showed high sensitivity and selectivity offering good potential in clinical diagnosis and disease treatment [149]. Chip‐ and probe‐based assays provide high specificity and multiplexing capacity but may require sophisticated fabrication processes, trained personnel, and specialized laboratory infrastructure.

Microarray‐Based Methods

3.2.4.6

Microarray‐based methods were developed for the sensitive and specific detection of large number of targets in single assay, such as different subtypes of influenza viruses and the simultaneous detection of different viruses in animals. These methods utilize features like sequence‐specific molecular beacons [150], enhanced sensitivity through nanogold‐streptavidin and silver‐stain nucleic acid dot‐blot hybridization system [151], and glycan‐based microarray biosensors to differentiate between animal and human–infective strains [152]. The microarrays demonstrated significantly higher sensitivity compared to conventional PCR, allowing for rapid and accurate detection of multiple avian influenza subtypes and other respiratory viral diseases [153]. Additionally, new methods for detecting antibodies and studying antigenic properties of influenza viruses were developed, showcasing their potential for comprehensive viral surveillance [153, 154]. A multiplex label‐free arrayed imaging reflectometry platform was developed for studying antigenic properties of influenza viruses and showed ability to detect various subtypes of IAVs [155]. Microarray platforms enable simultaneous detection of multiple targets, but they remain relatively costly, technically complex, and are not easily adaptable to on‐site diagnostics.

Electrical‐Based Methods

3.2.4.7

A few electrical‐based methods were developed and showed to be sensitive, fast, specific, and relatively cost‐effective. Peptide‐terminated electrodes were optimized to identify both seasonal and avian viruses [156], while a label‐free electrochemical paper‐based sensor demonstrated rapid detection of multiple AIVs antigens with good selectivity [157]. An enzyme‐free sandwich electrochemical immunosensor using graphene‐chitosan nanocomposites showed excellent performance for H9 AIVs detection providing a novel nonenzymatic method, which can be applied for the detection of other pathogens [158]. Additionally, a graphene oxide‐based immunosensor [159] and a graphene field effect transistor based ultrasensitive biosensor were developed [160], offering high specificity and sensitivity for avian influenza virus detection. These innovations highlight the potential for rapid, cost‐effective diagnostic solutions in clinical and field settings. Electrical biosensors offer speed and sensitivity, but many are still under development and face limitations in robustness, reproducibility, and validation for large‐scale surveillance use.

Sequencing and Metagenomics Methods

3.2.4.8

Sequencing methods were enhanced for in‐field applications aiming at eliminating transportation challenges and direct on‐site sequencing and subtyping of influenza viruses from a variety of field samples and starting materials. Long read random sequencing, particularly using the Oxford Nanopore Technologies MinION platform [161], has proven effective for rapid identification and genetic characterization of respiratory pathogens in clinical samples [162]. A processing pipeline was developed to identify DNA and RNA viruses and bacteria from clinical samples within 12 h [163]. Studies show that amplicon‐based nanopore sequencing achieved complete genome coverage for all RT‐PCR–positive IAV samples tested offering complementary advantages to RT‐PCR such as genomic characterization and strain subtyping [164]. Additionally, a targeted sequence capture panel improved sensitivity and depth for detecting porcine viruses [165], and in‐field nanopore sequencing allowed timely characterization of avian influenza strains collected from wild bird feces and poultry [166]. One study reported the first successful application of metagenomic Nanopore sequencing directly to clinical respiratory samples to detect influenza virus [167]. Genomic surveillance of AIVs has emerged as a promising cost‐effective and robust approach to understand virus transmission and evolution and for informing outbreak control efforts and policies [168]. One study applied metagenomic technology to avian virus surveillance using short read sequencing with Ion Torrent and showed that this method could simultaneously detect major viruses infecting farms [169]. Sequencing and metagenomic approaches provide comprehensive genetic information, yet they usually require bioinformatics expertise.

Commercial Panels and Developed Devices

3.2.4.9

A study showed that the QIAstat‐Dx Respiratory Panel could be used to detect zoonotic influenza A strains and differentiate them from the seasonal human seasonal IAVs [170]. Some research groups focused on developing devices for the detection of various diseases in a sensitive, specific, and timely manner, and with minimum sample handling and laboratory skill requirements.

One study validated a novel point of care diagnostic device utilizing photonic integrated circuits (PICs), microfluidics, and information and communication technologies for the detection of swine IAVs using spiked and clinical oral fluid samples and reported high sensitivity and specificity [171]. Another portable quantitative device based on giant magnetoresistive (GMR) technology was developed for detecting sensitively and specifically swine IAVs with minimum sample handling and laboratory skill requirements [172]. Commercial panels and portable devices can simplify diagnostics, but the need for validation across diverse field conditions may restrict widespread adoption.

Effectiveness in Improving Risk Assessment on Human Infection by Animal IAVs

3.2.5

Readily available the WOAH‐World Animal Health Information System (WAHIS), public surveillance data and time‐series models were used to build an early warning model to predict the detection of H5 HPAI with two seasonal waves in the endemic component and three seasonal waves in the epidemic component. The early warning model showed flexibility to be used even in countries with large temporal variation in the number of HPAI detections to predict detections. This model is capable of monitoring the weekly risk of HPAI outbreaks within European countries to support decision making and timely implementation of preventive measures [173].

Discussion

4

The 2017 WHO public health research agenda for influenza recommended the development of more sensitive and specific influenza virus surveillance and detection systems in farmed animals and wildlife, including epidemiological designs and novel technologies. Since 2017, many countries have developed novel influenza surveillance systems or have introduced enhancements to their existing surveillance programs to improve monitoring of influenza viruses and enhance early detection and response. In this report, we summarized enhanced and novel surveillance methods and technologies applied in domestic animal husbandry and exposed humans and in wildlife and their outcomes and highlighted gaps. While this review focused specifically on novel and enhanced surveillance methodologies, these findings also indirectly reflect the broader status of zoonotic influenza surveillance by highlighting how methodological advances have addressed some gaps while leaving others unresolved.

In domestic animals, over the last 8 years, few predictive surveillance systems were developed focusing mainly on assessing the risk of introduction of zoonotic influenza into domestic animals from contact with wild animals using influenza transmission, demographic, and spatial risk factors and few research studies reported participatory community‐based surveillance systems for emerging zoonoses incorporating demographic and behavioral factors under the One Health approach. Surveillance systems in domestic animals have gained scientists' interest during the last decade and enhancements have been introduced, especially focusing on environmental noninvasive sampling approaches. Integrating data management, bioinformatics, and biosafety practices should be considered by the research community for the development of effective surveillance systems as well as combining surveillance data within risk assessment frameworks to better assess zoonotic and pandemic potential of novel IAVs strains and to inform mitigation measures. Research studies conducted to develop One Health surveillance systems were sparse. Thus, more attention should be given to One Health surveillance systems by integrating human, animal, and environmental data. This need for integrated, forward‐looking surveillance systems aligns with the priorities highlighted in recent WHO R&D Blueprint consultations on H5N1, which emphasized the urgency of strengthening genomic surveillance, rapid data sharing, and the development of candidate vaccines, therapeutics, and diagnostics to prepare for potential pandemic spread [174].

Only two digital dashboards have been developed as analytical interactive tools for zoonotic influenza monitoring aiming at enhancing the accessibility of data and information for policy makers and stakeholders to formulate policy and ensure appropriate mitigation measures. Research should focus on the use of mobile health technologies and smart applications for reporting of infections in humans and engage communities in surveillance efforts.

In wild animals, few predictive surveillance systems were developed for monitoring and isolating zoonotic influenza from wild birds by studying geographic factors, spatiotemporal models, ecological nature of wild birds, and observing the health of wild birds. A few sampling approaches in wild birds were developed that aimed at improving early detection of zoonotic influenza in wild birds to protect local animal population focusing on minimizing invasive methods. Novel environmental sampling methods were developed globally from 2017 to 2024 to improve the detection of influenza viruses in wild animals, improve monitoring of those viruses, and creating transmission risk maps. Research should focus on making those novel environmental surveillance methods useful to public health authorities. Surveillance should be increased in animal populations through using risk assessment studies and findings to implement regular sampling and surveillance of wild birds and domestic animals in high‐risk areas. Furthermore, the utilization of risk assessment tools and mathematical modelling should be enhanced to anticipate the spread of zoonotic IAVs and predict the emergence of novel influenza strains using environmental, demographic, and epidemiological data.

Innovations in field and laboratory testing for influenza viruses have significantly bolstered both animal and human surveillance. Advances in molecular techniques, particularly RT‐qPCR and next‐generation sequencing, have improved the rapid detection and genetic analysis of viral strains. Additionally, the emergence of next‐generation sequencing and genomic surveillance has facilitated the genetic analysis of viral strains, aiding in understanding their origins and spill‐over risks. Sensitive nucleic acid amplification methods, multiplex PCR, and insulated isothermal RT‐PCR enhance diagnostic capabilities. Further developments include RT‐LAMP assays, surface‐enhanced Raman spectroscopy–based methods, and immunochromatographic kits for rapid detection. ELISA methods using monoclonal antibodies and chip‐based diagnostics were developed to improve detection for diverse applications. Enhanced sequencing methods facilitate in‐field applications, enabling direct on‐site analysis of influenza viruses. Additionally, an early warning model utilizing public surveillance data has been developed to predict HPAI outbreaks in Europe, supporting timely preventive measures. Overall, these advancements enhance the capacity for effective influenza surveillance and response. Although RT‐PCR remains the gold standard technique for sensitivity and specificity, the new technologies described in this review offer complementary advantages such as faster turnaround times, reduced infrastructure requirements, multiplexing capacity or true portability in the field. Pursuing these technologies is therefore important to strengthen decentralized surveillance, improve outbreak response in resource‐limited settings, and provide additional layers of data to guide risk assessment. Updating developed molecular‐based diagnosis tests for zoonotic influenza regularly to cover the continuous genetic assortment of zoonotic, swine, and avian influenza viruses and combining molecular‐based techniques with nanotechnology to develop new diagnostic tests for zoonotic influenza are recommended. Sequencing methods were enhanced for in‐field applications aiming at eliminating transportation challenges and direct on‐site sequencing and subtyping of influenza viruses from a variety of field samples and starting materials. These methods have also been applied for multiplexing and for metagenomics for the detection of DNA and RNA viruses and bacterial pathogens from clinical samples. Additional research should be focused on enhanced genomic surveillance and the development of portable sequencing technologies for on‐site rapid identification of viral strains in remote areas and wildlife. Moreover, big data analytics and machine learning algorithms should be applied in addition to digital health records to monitor trends in influenza occurrence and detect anomalies and predict outbreaks. Some research groups focused on developing devices for the detection of various diseases in a sensitive, specific, and timely manner, and with minimum sample handling and laboratory skill requirements. Such devises could be used as fieldable, cost‐effective tools to complement laboratory‐based gold standard assays, especially where rapid decision making is needed. Research should focus on developing new rapid, sensitive, and inexpensive portable detection kits for field influenza virus detection and surveillance with subtyping capacities. Taken together, the methodologies reviewed vary in their suitability for different surveillance contexts. Nucleic acid amplification assays and RT‐qPCR remain best suited for laboratory‐based diagnosis, while isothermal methods (e.g., RT‐LAMP, insulated isothermal PCR) hold promise for use in resource‐limited or field settings. Optical‐ and nanoparticle‐based technologies demonstrate high sensitivity in proof‐of‐concept studies but require further validation before widespread adoption. Sequencing approaches, while generally less sensitive for low viral loads, provide essential genomic data that is particularly valuable for risk assessment and outbreak investigations. For wildlife surveillance, noninvasive sampling approaches are most practical and ethically feasible, whereas for farm settings, pooled environmental and group sampling methods have shown the greatest efficiency.

One early warning model using available WOAH‐WAHIS HPAI public surveillance data and time‐series models was developed to predict the detection of A(H5) HPAI and monitor the weekly risk of HPAI outbreaks within European countries to support decision making and timely implementation of preventive measures. More research on assessing the effectiveness of improving risk assessment on human infection by animal influenza A viruses should be conducted. Enhancing environmental monitoring of influenza viruses and applying remote sensing technologies to track migratory patterns of influenza virus reservoir are recommended.

The key gaps highlighted by this review include insufficient integration of One Health data streams, limited application of noninvasive and environmental sampling approaches, inadequate validation and deployment of emerging diagnostic technologies, and sparse evaluation of how novel methodologies inform risk assessment and decision making. Addressing these gaps should be prioritized in future research and international surveillance initiatives.

A limitation of this review is the reliance on PubMed as the primary database, which may have biased the included literature toward English‐language publications and internationally indexed journals. As a result, surveillance studies reported in local or regional journals, particularly in non‐English languages, may have been missed. This limitation could partially explain the underrepresentation of certain geographic regions in the included studies.

Conclusions

5

The evolution of influenza surveillance systems since the 2017 WHO public health research agenda update has led to advancements in monitoring both domestic animals and wildlife. Analysis of the collected publications indicated several gaps at different levels of surveillance and unmet public health needs. Most countries do not have active and comprehensive surveillance programs for zoonotic influenza at the human–animal interface, which underestimates the true burden of zoonotic influenza diseases. This conclusion is supported by the geographic distribution of studies included in this review, which shows concentrations in East/Southeast Asia, Europe, and North America, but very limited representation from Africa, South America, and the Middle East. Although there has been progress in developing predictive surveillance systems and integrating innovative technologies, notable gaps remain, particularly in the context of One Health approaches that encompass human, animal, and environmental data. The development of participatory community‐based surveillance and the use of digital tools, such as mobile health technologies, are essential for engaging communities and improving data accessibility for policymakers.

Further research is needed to refine and enhance the effectiveness of these surveillance systems, particularly through risk assessment frameworks that can anticipate zoonotic threats. The novel environmental sampling methods and advances in molecular diagnostics, such as real‐time PCR and next‐generation sequencing, have bolstered detection capabilities but require ongoing updates to account for the genetic diversity of influenza viruses. Moreover, the application of big data analytics and machine learning could improve the predictive power of surveillance systems, allowing for timely interventions. Emphasizing the importance of portable and cost‐effective diagnostic tools will facilitate rapid field detection, enhancing our capacity to respond to outbreaks effectively. Our findings underscore the need to focus on filling gaps in One Health integration, validation and field deployment of novel assays, expansion of noninvasive and environmental surveillance, and stronger linkage of methodological innovations to risk assessment and policy action. As we move forward, prioritizing comprehensive surveillance efforts will be crucial in mitigating the risks posed by zoonotic influenza and safeguarding public health under the One Health approach.

Author Contributions

Rebecca Badra: conceptualization, methodology, validation, data curation, writing – original draft, writing – review and editing. Wenqing Zhang: conceptualization, funding acquisition, project administration, writing – review and editing. John S. L. Tam: conceptualization, writing – review and editing. Richard Webby: writing – review and editing. Sylvie Van Der Werf: writing – review and editing. Sergejs Nikisins: project administration, writing – review and editing, resources. Ann Cullinane: writing – review and editing. Saad Gharaibeh: writing – review and editing. Richard Njouom: writing – review and editing. Malik Peiris: writing – review and editing. Ghazi Kayali: conceptualization, methodology, data curation, writing – review and editing, supervision. Jean‐Michel Heraud: conceptualization, supervision, project administration, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.