Introduction

Animal diseases, whether affecting livestock, wildlife, or companion animals, pose complex challenges at different scales. Beyond their direct impact on animal health, these diseases can lead to significant economic losses for agricultural producers and also affect the animal health industry, tourism, and even international trade by restricting exchanges [1,2]. Moreover, some of these diseases are transmissible to humans (zoonoses), thereby increasing global health risks and underscoring the importance of adopting a “One Health” approach that integrates human, animal, and environmental health [3].

The challenges posed by animal diseases are intensifying due to phenomena such as globalization, increasing urbanization, antimicrobial resistance, deforestation, climate change, and the fragmentation of natural habitats [4]. These factors promote the spread of animal diseases both within livestock systems and among wildlife, increasing the risk of emerging zoonoses. The growing interactions between humans and domestic or wild animals, particularly in expanding urban and rural environments, also heighten the risk of disease transmission [5,6]. Thus, effective management of animal diseases should not be limited to protecting livestock but should also encompass wildlife and companion animals, with a focus on public health and ecological balance. It requires a comprehensive and integrated approach involving multiple stakeholders, including farmers, veterinarians, the livestock industry (cooperatives, slaughterhouses, transport companies), wildlife managers, public authorities, and pet owners [7,8]. However, the absence of robust and equitable global governance mechanisms for animal health limits the ability to respond effectively to these challenges, compromising the management of zoonotic diseases and global health equity objectives [9,10].

Furthermore, despite various efforts, including the implementation of international regulatory frameworks, additional gaps remain in animal disease management programs. These gaps pertain to diagnostics, surveillance, biosecurity, vaccination systems, culling, epidemic management, and national and international coordination [5,11-12–]. In particular, combating animal diseases in regions with limited veterinary resources, as well as in wildlife species for which management tools are less developed, represents a considerable challenge [13]. Additionally, the increasing emergence of antimicrobial resistance (AMR) threatens both animal and human health. The intensive and sometimes inappropriate use of antibiotics in livestock systems and in domestic animals contributes to this resistance, exacerbating the difficulty of effectively treating certain infections [14,15].

Beyond technical and health-related aspects, the management of animal diseases is also influenced by socio-economic factors, which play a crucial role in the adoption and effectiveness of control measures. The behaviors, practices, beliefs, and perceptions of different stakeholders, as well as social organization and contractual relationships, whether among farmers, veterinarians, pet owners, or policymakers, can either facilitate or hinder the implementation of disease prevention and management strategies [16]. These aspects, often overlooked, must be incorporated into a global governance framework where animal health is currently relegated to the background compared to human health.

There has been a recent uptake of social sciences in veterinary public health. Animal Health Economics, through the onset of international societies and global programme such as GBADS (Global Burden of Animal Diseases) has been one of the first disciplines investigating the importance of economics for decision making in animal health management [17]. Recognizing the bounded rationality of factors, sociology and political science have also explored the challenges faced in veterinary public health. However, most research on these factors falls under the concept of “acceptability,” which can be understood as an individual’s assessment of an intervention’s relevance but may also reflect a broader collective judgment on the nature of a health intervention [18]. This concept is generally limited to incorporate in epidemiology studies, individual and collective behaviors, and their impact on the implementation of intervention measures, particularly through Knowledge, Attitudes, and Practices (KAP) studies [19]. It frequently fails to account for more complex social dynamics that influence these behaviors. These may include aspects such as power relations, cultural norms, economic inequalities, and access to resources, all of which significantly shape individuals’ perceptions and reactions to health interventions. By neglecting these dimensions, traditional approaches risk providing a fragmented understanding of the obstacles to adopting control measures, thereby limiting the effectiveness of implemented strategies. Thus, identifying and understanding these determinants is crucial not only for improving disease management but also for elucidating how broader social factors shape decision-making processes. Indeed, decision-making in animal health management results from a complex interplay of social, economic, and institutional factors that define the boundaries between public and private interests.

In this study, we used the socio-ecological model to propose a new classification of key determinants that influence the adoption of health management measures, considering local contexts and the specific dynamics of different systems (livestock, wildlife, and companion animals). We refer to these determinants as “applicability factors”, meaning the contextual and socio-economic conditions that can either enable or constrain the effective implementation of animal health management measures. To develop this typology, we conducted a scoping review of the literature. The scoping review was conducted following the guidelines established in the PRISMA-ScR statement. By refining the analysis enablers and barriers to implementation, this framework supports the design of more effective and sustainable management strategies. Furthermore, by offering concrete pathways to strengthen animal health governance, our work contributes to enhancing governance frameworks on a global scale. Given the growing recognition of the interconnections between animal and human health, our approach fosters the development of integrated health policies that are responsive to both global and local challenges.

Materials and methods

Results

Article search and selection

The search conducted in the Web of Science and Scopus databases yielded a total of 22,683 articles (9,010 from WOS and 13,673 from Scopus). The removal of duplicates reduced the number of selected articles to 17,652. The screening phase, based on title review, led to the exclusion of 15,199 articles, while the abstract review resulted in the exclusion of 1,634 articles. A total of 819 articles were retained for full-text review. Following this final selection step, 593 articles were included for analysis in this study (Fig 1). The articles were published between 2010 and 2025. Our results indicate a growing number of publications over this period, with a peak of interest in 2024 (12%) and 2020 (9%) (see S6 Table and S1 Fig).

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Fig 1. PRISMA diagram.

https://doi.org/10.1371/journal.pone.0344746.g001

General characteristics of the selected articles

Geographical scope.

The 593 articles included in this study cover data from 91 countries (Fig 2). Most studies were conducted in high-income countries, particularly the United Kingdom (7%), Australia (4%), the United States (6%), China (3%), and France (3%). Some research stemmed from low- and middle-income countries, such as Nigeria (3%), Ethiopia (4%), Tanzania (3%), and India (2%). Most of these studies were conducted at the national level (24%), followed by farm-level (15%), regional (17%), local (16%), and individual-level (8%) studies. Additionally, some studies were conducted at broader geographic scales, with 8 studies focusing on the African continent, 12 on Europe, and 5 on Asia (see S7 Table).

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Fig 2. Distribution of studies by country.

Eight studies in Africa, 4 studies in Latin America, 5 studies in Asia, 1 study in developed countries, 1 study in developing, countries 12 studies in Europe, 13 studies worldwide, 3 studies in LMICs, 1 study in the Mediterranean Basin, 1 study in North Africa and the Middle East, 1 study in Sub-Saharan Africa, 1 study in South America, 1 study in South Countries, 1 study in East Africa, 1 study in Caribbean region, 1 study in Asia-Oceania countries and 2 studies in South East Asia. The figure was generated using the rnaturalearth package in RStudio.

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Academic disciplines.

The disciplines involved in the studies identified were mainly epidemiology (29%), social sciences (32%), economics (19%), and life sciences (12%) (Table 1). Included studies were predominantly qualitative (31%), with a strong representation of survey-based research (21%). Quantitative analyses (21%) were also common, including modeling studies (5%) (Table 1). Furthermore, the majority of studies (67%) were conducted in routine contexts, outside of emergency situations. (Table 1) The complete characteristics of the studies are available in S8 Table.

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Table 1. Summary of the five most represented among included studies.

https://doi.org/10.1371/journal.pone.0344746.t001

Disease coverage.

Included studies provide data on 53 animal diseases. Among the most studied diseases, rabies is the most represented, with 146 identified studies (25%), followed by avian influenza (17%), bovine tuberculosis (11%), foot-and-mouth disease (11%), and African swine fever (8%) (Table 1). Regarding studied species, cattle are the most represented (24%), followed by dogs (22%), poultry (20%), and pigs (16%). This distribution reflects the prevalence of the targeted diseases in our dataset (Table 1). Some studies also focus on wildlife species, such as badgers (1%), wild boars (2%), and foxes (0.7%), which play a key role in disease transmission, as seen with wild boars and African swine fever. However, the majority of research remains centered on livestock species (69% of the dataset). Detailed information on the diseases and species represented in our study is available in S8 Table.

Animal health interventions.

In our study corpus, vaccination emerged as the most extensively studied management measure, identified in 234 out of 593 studies (39%) (Table 1), followed by biosecurity strategies (32%) and surveillance programs (22%). Regarding the stakeholders involved, farmers were the most represented, accounting for 51% of the studies, followed by citizens (11%), veterinarians (11%), and dog owners (8%) (Table 1). A complete list of management measures and the stakeholders involved is available in S9, S10 Tables and S2, S3 Figs.

Factors influencing the applicability of animal health management measures

Using the theoretical framework of the socio-ecological system, we established five key categories of factors that influence the implementation of animal health management measures. These categories allowed us to classify the relevant articles from the literature. The five categories include: (1) individual and socio-cognitive factors, (2) organizational and professional factors, (3) economic factors, (4) socio-political and institutional factors, and (5) infrastructural factors. This new classification is based on the premise that these factors must be considered interactively to fully understand the application of animal health management measures. Indeed, their implementation depends on the continuous interplay of these factors and their influence on decision-making by the various stakeholders involved. Data extracted from the literature highlighted the predominant role of individual and socio-cognitive factors (56%), underscoring the crucial importance of stakeholders’ perceptions, knowledge, and attitudes in the adoption of management measures (see S11 Table).

Fig 3 illustrates the most frequently mentioned terms identified through lexical analysis of the extraction grid for each factor. These terms derive from the standardized information recorded during data extraction process (see S12 Table). A detailed description of each factor is provided in the following sections to offer a comprehensive understanding of their role in animal health management. The frequency analysis is used here as an exploratory and illustrative tool to visually and systematically highlight recurring themes across a heterogeneous body of literature, rather than to infer quantitative importance. In this respect, it complements the descriptive statistical results by revealing how applicability factors are most commonly framed and discussed in the reviewed studies. Similar approaches have been applied in veterinary science, where text mining methods have been used to extract and synthesize recurring patterns from diverse sources, including online forums [34] and veterinary clinical records [35].

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Fig 3. Most frequent terms identified through lexical analysis of the extrication grid by applicability factors.

A) Top 10 words for economic factors; B) Top 10 words for individual and socio-cognitive factors; C) Top 10 most frequent words for infrastructural factors; D) Top 10 words for organizational and professional factors; E) Top 10 words for socio-political and institutional factors.

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Individual and socio-cognitive factors.

Individual and socio-cognitive factors refer to the set of personal characteristics, knowledge, beliefs, attitudes, and perceptions that shape stakeholders’ understanding and decision-making regarding animal health management measures. This category encompasses both cognitive elements, such as disease knowledge and risk perception, and socio-demographic attributes, including age, gender, education level, farming experience, and household characteristics, which influence how individuals interpret information and act upon it. The most frequently mentioned terms were “knowledge,” “representation,” “level,” and “education.” Studies consistently show that stakeholders’ knowledge and awareness determine their recognition of the importance of preventive and control measures, such as vaccination and biosecurity protocols [36,37]. Past experiences and cultural traditions often shape attitudes and beliefs, leading some livestock farmers to favor traditional practices over modern veterinary interventions [38,39]. Trust in authorities and veterinary professionals is a key determinant, with higher trust facilitating compliance and skepticism limiting adoption [40]. Additionally, risk perception and risk aversion influence prioritization of preventive versus reactive measures [41], while social dynamics, including peer influence and fear of judgment, further affect individual decision-making [42]. Together, these factors critically shape the adoption and consistency of management measures, highlighting the importance of understanding individual knowledge, attitudes, perceptions, and socio-demographic characteristics when designing effective disease control strategies.

Organizational and profession factors.

Organizational and professional factors are defined as the characteristics of the structures, practices, and capacities of the organizations and professionals involved in animal health, including veterinary services, professional associations, and farmers’ networks. This category encompasses aspects such as coordination between stakeholders, availability and quality of professional support, access to trained personnel, and adequacy of equipment and resources. The most frequently cited terms were “lack,” “vaccination,” “veterinary,” “information,” and “service.” Numerous studies emphasize that effective interactions among stakeholders, particularly between farmers and veterinarians or technical support systems, are essential for the timely dissemination of information and the implementation of best practices [43]. A lack of coordination and communication between these actors has often been reported as a barrier to early disease detection and efficient management [44]. In addition, limited capacity of veterinary services, poor-quality provision, shortages of qualified personnel, and insufficient equipment further constrain the ability to respond to health crises effectively [45,46]. Recent global mapping of access to veterinary services further illustrates how uneven distribution of veterinary practices and limited availability of trained personnel create significant barriers, particularly in low- and middle-income countries, while also highlighting the strong variability of these organizational and professional determinants across contexts [47]. These findings appear across multiple contexts and countries, highlighting that organizational and professional factor represent systemic determinants with broad implications for the adoption and success of disease management measures.

Economic factors.

For economic factors, the key terms were “cost,” “economic,” “impact,” “financial,” and “vaccination.” These factors primarily relate to the costs associated with management measures, financial losses due to animal diseases, trade and market impacts, economic incentives, and financial constraints. The cost of implementing management measures is a major factor influencing their feasibility, as it is largely borne by farmers, who often perceive the return on investment as uncertain [48,49]. Financial losses linked to the adoption of these measures discourage farmers from applying them, especially when no financial compensation is provided by governments [50]. Studies have shown that demonstrating the economic benefits of implementing management measures, along with offering financial compensation, could encourage farmers to adopt them [51]. Furthermore, research has highlighted a lack of dedicated funding for animal disease management, particularly in resource-limited countries, which hinders the effective implementation of necessary measures [52,53].

Socio-political and institutional factors.

In the socio-political and institutional category, the most common words were “measure,” “political,” “lack,” “coordination,” and “government.” These factors encompassing policies, regulations, and institutional frameworks that influence animal disease management. A lack of communication and coordination among stakeholders, who often have diverging interests, particularly between public health authorities and veterinary services, has been widely documented [54]. Establishing entities that facilitate dialogue while integrating scientific knowledge and local needs is essential to ensure that community-specific factors are considered in decision-making processes [55]. This approach can enhance the acceptance and implementation of management measures. Decentralization and subsidiarity have emerged in several studies as potential solutions to address local specificities and improve information dissemination [56]. Additionally, political instability and armed conflicts can further hinder the implementation of disease management measures [48]. Public authorities play a crucial role in disseminating health regulations and raising community awareness. They must also provide essential resources, such as access to training programs, to strengthen animal disease management strategies [57].

Infrastructural factors.

Finally, for infrastructural factors, the most prevalent terms were “system,” “livestock,” “farm,” “lack,” and “animal.” These factors were identified as essential components of animal disease management, encompassing physical infrastructure such as veterinary clinics, quarantine facilities, laboratories, and vaccination sites. The structure of livestock farms has been documented as influencing the implementation of disease management measures. A frequent issue is the lack of space to isolate sick animals and regulate herd populations, which hampers disease control efforts [58]. Additionally, limited laboratory capacity poses a major challenge, with shortages of diagnostic tests, treatments, and even vaccines restricting effective disease management [59]. Furthermore, the limited availability of veterinary services, particularly in remote areas, makes access to animal healthcare difficult for farmers [60]. Fragmented health systems further complicate the implementation of management measures by restricting access to essential treatments and vaccines [61].

Some words appear across multiple categories, such as the term “lack,” which is found in every category, and “vaccination,” which is mentioned in the infrastructural, economic, and organizational and professional factors. This highlights the value of a multifactorial approach, underscoring the complex and interconnected nature of the factors that influence the implementation of animal health management measures. Fig 4 illustrates all the factors defined in this study and their relative significance within our corpus.

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Fig 4. Applicability factors of animal diseases management.

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Discussion

Non-technical factors play a pivotal role in the implementation of any policy. Individual and collective behaviors, along with decision-making processes, directly influence the effectiveness of interventions. These behaviors are shaped by determinants that interact across multiple levels [23,24]. To analyze how these factors affect stakeholders’ decision-making in animal health, particularly regarding the implementation of disease management measures, we used the socio-ecological model as our theoretical framework. Originally developed in developmental psychology [20] and later widely applied in public health [22], this model emphasizes that individual behaviours are embedded within, and shaped by, multiple interacting levels of influence, from individual cognition to institutional, infrastructural, and policy contexts. Building on this foundation, we defined categories of applicability factors tailored to the veterinary field, incorporating dimensions such as veterinary services, professional practices, and infrastructures as distinct components. This adaptation was conducted prior to data encoding and subsequently consolidated through our scoping review, which allowed us to test the relevance of this framework against a heterogeneous body of literature. By combining a robust conceptual model with empirical validation, we introduced a classification of applicability factors that either facilitate or hinder the adoption of management measures. Importantly, this approach moves beyond a compartmentalized reading of determinants: the factors we identified do not operate in isolation but interact dynamically across levels. Overlooking these interactions risks oversimplifying the complexity of animal health governance and underestimating systemic barriers to effective disease management. Our contribution therefore lies in providing both a novel analytical lens and an empirically grounded typology to guide the study of applicability factors in animal health. This integrated perspective highlights the need for context-specific, multi-level interventions that recognize systemic interdependencies and can support more coordinated and equitable governance within a One Health framework.

A scoping review was conducted, identifying 593 articles included in this study. We chose this method to allow for a broad examination of available studies, in contrast to more traditional systematic reviews, such as meta-analyses. Additionally, this approach is useful for identifying knowledge gaps and mapping evidence on a given topic [29]. However, while this method enabled us to include a wide variety of studies, it may also have resulted in variability in the methodological rigor of the selected research, with some studies being less robust than others. Furthermore, our selection was limited to texts in English and French, which may have excluded relevant research published in other languages. This could introduce a language bias and limit the diversity of perspectives on the studied topic. We also chose to restrict the data extraction period to studies published between 2010 and 2025, allowing us to focus on recent work. However, this decision excluded older studies that might have provided important historical context or additional insights. Finally, the categories of applicability factors were defined before data extraction, based on the principles of socio-ecological model. While this approach allowed for a structured and coherent analysis, it may have introduced a selection bias by influencing how we interpreted and classified study results. A more inductive approach could have facilitated the identification of unanticipated applicability factors.

Our analysis reveals a strong overrepresentation of studies from developed countries, particularly economic powers such as the United Kingdom, the United States, Australia, China, and France. This trend reflects their substantial financial, institutional, and scientific resources, facilitating research on animal disease management [11]. Additionally, their structured livestock industries necessitate robust health control strategies to maintain productivity and trade relations [62,63]. Consequently, intensive livestock systems dominate the literature, while extensive systems, more common in the Global South, remain underrepresented [63]. Developing countries, despite their high exposure to health risks, face limited research due to budget priorities favoring human health and a lack of veterinary infrastructure and surveillance programs [13]. This imbalance highlights gaps in knowledge on local constraints and solutions tailored to low-resource settings. Strengthening international cooperation and investing in data collection are crucial to enhancing disease control and pandemic preparedness, especially within the One Health framework, which recognizes the interdependence of animal, human, and environmental health.

Our results also highlight an overrepresentation of certain diseases, rabies, avian influenza, bovine tuberculosis, and foot-and-mouth disease, compared to others such as brucellosis or salmonellosis. This bias is largely driven by economic and health impacts, trade implications, and zoonotic risks [64,65]. For instance, avian influenza and foot-and-mouth disease impose heavy economic losses due to movement restrictions and culling measures [66,67]. Rabies and bovine tuberculosis, given their zoonotic potential, are prioritized in international programs, such as the 2015 WHO-OIE-FAO-GARC initiative to eliminate rabies by 2030 [68]. Additionally, livestock species receive significantly more research attention than companion animals, aquaculture, or beekeeping, which are often overlooked as a form of “undone science” [69], reflecting the economic dominance of the livestock industry [70]. Neglecting these underrepresented species and diseases may pose future risks, particularly as climate change and globalization alter disease dynamics. Additionally, vaccination emerges as the most studied disease management strategy in our corpus, widely recognized for its role in controlling infectious diseases, as seen in the eradication of rinderpest and smallpox [71]. Vaccination campaigns, particularly for zoonoses like rabies, are crucial, yet their implementation faces challenges. Resistance from farmers and consumers, ethical concerns regarding animal testing, and the financial burden of large-scale campaigns remain key obstacles [71]. Furthermore, serological interference in vaccinated animals can lead to trade restrictions, as observed with avian influenza and foot-and-mouth disease [71]. Our findings also underscore the central role of farmers in disease management, while other key stakeholders, veterinarians, health authorities, and policymakers, appear less involved in implementing control measures. Effective disease control requires coordinated action from all actors, including researchers, international organizations, and consumers [72,73]. However, structural barriers such as weak regulatory frameworks, resource constraints, and limited veterinary access hinder broader engagement [74]. Addressing these challenges is crucial for a more inclusive and effective approach to animal health governance.

This study proposes a new typology of applicability factors influencing animal disease management measures. We identified five categories, reinterpreting the socio-ecological model to integrate key dimensions such as power dynamics, resource access inequalities, and the bidirectional nature of influences across levels as shown in Fig 4. This approach moves beyond a simplistic behavioral perspective, providing a more nuanced understanding of the facilitators and barriers to disease management. Rather than adhering to the traditional nested and hierarchical structure of the socio-ecological model [75], commonly used in public health, we emphasize the complex interplay between these factors. We identified individual and socio-cognitive factors that shape the adoption of management measures, particularly through actors’ knowledge and perceptions of health risks [41]. While awareness initiatives improve adoption [76], ignorance, erroneous beliefs, and cultural resistance can hinder implementation [39–77]. These factors are often analyzed using the Knowledge, Attitudes, and Practices (KAP) approach, which tends to be reductionist by focusing primarily on individual behavior changes while overlooking structural constraints and power dynamics. As shown in Fig 3, which presents the most frequently occurring words for each factor, terms such as knowledge, representation, education, awareness, behavior, practice, and age are the most prominent in this category, perfectly illustrating the prevalence of KAP-based studies. However, actors operate within institutional and regulatory frameworks where political and industrial interests may not align with their needs [78,79]. Moreover, such models often assume a one-way influence of policies on behaviors, overlooking feedback loops and the capacity of local practices to reshape governance frameworks. By ignoring socio-economic inequalities, they risk masking mechanisms of marginalization that restrict access to veterinary resources and services, further complicating effective disease management [80].

Thus, we also identified organizational and professional factors, particularly the availability and quality of veterinary services, as well as interactions between stakeholders, such as farmers, peers, and health or technical advisors. Effective coordination among these actors is crucial to ensuring seamless information transfer and fostering the adoption of management measures. Strengthening veterinary services, through improved infrastructure, equipment, and staff training, is essential to enhancing disease control efforts. Additionally, we emphasized the critical role of economic factors, particularly the costs associated with management measures and the availability of resources for their implementation. This is especially relevant for farmers, who may be reluctant to adopt such measures if they do not perceive immediate benefits. Financial mechanisms, such as compensation, have been shown to facilitate adoption [50]. This is further illustrated in Fig 3, where the most frequently occurring terms in this category include cost, economic, impact, financial, and compensation. However, it is crucial to go beyond simple cost-benefit analysis by considering socio-economic inequalities that may obscure marginalization mechanisms, preventing certain groups from accessing the necessary resources and veterinary services for effective disease management. These factors are also linked to socio-political and institutional factors, which emphasize the importance of clear norms and policies to facilitate the adoption of measures. Coordination among institutional actors is essential, as divergences or lack of communication between these entities can hinder the effective implementation of measures [53]. Therefore, incentive policies and collaborative governance mechanisms could enhance the implementation and execution of disease management interventions. Finally, understanding the role of infrastructural factors is crucial. The reviewed studies highlight the impact of equipment availability, such as diagnostic tests, storage facilities for medicines and vaccines, and access to new technologies, on the adoption of management measures [81]. Conversely, inadequate infrastructure can be a major barrier, particularly when limited farm space prevents the isolation of diseased animals [59]. Additionally, the geographical isolation of certain farmers restricts access to veterinary services, diagnostic tools, and treatments, further complicating disease management [60].

Building on the socio-ecological model, it is important to emphasize that the five categories of applicability factors do not operate in isolation. Individual and socio-cognitive factors are influenced by, and in turn influence, organizational, economic, institutional, and infrastructural contexts, creating a network of interdependent determinants. For example, a farmer’s knowledge and perception of disease risks may shape their adoption of vaccination, but these behaviors are constrained or facilitated by the availability and quality of veterinary services, access to financial resources, and supportive institutional policies. Similarly, institutional regulations and governance structures can enable or restrict professional practices and resource distribution, which subsequently affect individual-level behaviors. By framing animal health management within this interactive socio-ecological lens, we highlight the bidirectional and feedback relationships across levels: interventions at the policy or organizational level can modify individual behaviors, while collective experiences and local practices can, over time, influence institutional and governance frameworks. Recognizing these interdependencies underscores the need for integrated, multi-level strategies that align behavioral, organizational, economic, and infrastructural levers, rather than isolated interventions, to effectively enhance the adoption and implementation of disease management measures.

These findings highlight the necessity of a multifactorial approach to animal disease management, ensuring that key factors are integrated into decision-making rather than treated as external constraints (Fig 4). Neglecting this complexity risks leading to reductionist strategies that overlook the diverse actors and interactions involved, ultimately undermining the effectiveness and sustainability of disease control efforts. Our classification of applicability factors underscores the critical role of information access and dissemination in shaping the adoption of management measures. Effective peer communication fosters knowledge-sharing and collective action, while the spread of information, whether accurate or misleading, can significantly influence adherence to health measures, particularly in the digital age. While social media can enhance awareness, it also facilitates misinformation, eroding trust in authorities and encouraging risky behaviors [82]. This was evident during the COVID-19 pandemic, where conspiracy theories fueled vaccine hesitancy and resistance to public health measures [83]. Similar challenges arise in animal health, particularly regarding vaccine safety, treatment efficacy, and biosecurity compliance. To address these issues, health authorities must adopt proactive communication strategies that ensure transparent and coherent dissemination of reliable information. Integrating these elements into disease management strategies is essential for improving compliance and health outcomes. A robust governance framework, built on coordinated policies, interdisciplinary collaboration, and effective risk communication, can strengthen disease control efforts and enhance the resilience of animal health systems.

Conclusion

In conclusion, the proposed framework constitutes an initial step toward systematically integrating multi-level and interdependent determinants into animal health management, with future applications expected to refine its categories and demonstrate their interactions in practice. This research highlights several key points that resonate with the global challenges we face today in public health, particularly in the context of global health governance. Global health relies on a systemic approach, emphasized by the One Health approach, which recognizes the interdependence of human, animal, and environmental health factors. This perspective is particularly relevant when analyzing the factors that influence the adoption of animal disease management measures. Indeed, these factors are not limited to veterinary or zootechnical aspects alone but are also shaped by social, political, economic, and organizational dimensions. All of the factors identified in this review are part of a broader dynamic of global health, where the management of animal diseases cannot be separated from human health management. In an era where health challenges are increasingly global, it is essential to adopt a One Health approach that recognizes the interconnection between these different spheres of health. Such an approach will help better understand and overcome the obstacles to adopting more sustainable and suitable management measures. Integrated public policies that consider the economic, social, and infrastructural realities of animal disease management systems are essential. Interventions tailored to the local level aimed at improving stakeholder perceptions are necessary to facilitate the adoption of management measures.

Supporting information