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Record W4408226450 · doi:10.3389/fvets.2025.1571267

Editorial: Estimating non-monetary societal burden of livestock disease management

2025· editorial· en· W4408226450 on OpenAlexaff
Chisoni Mumba, Guillaume Lhermie, Karl M. Rich

Bibliographic record

VenueFrontiers in Veterinary Science · 2025
Typeeditorial
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLivestockBurden of diseaseDisease managementNatural resource economicsDisease burdenDiseaseAgricultural economicsEconomicsMedicineBiologyEcologyInternal medicine

Abstract

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IntroductionAnimal diseases significantly affect various aspects of society, including agriculture, public health, and environmental sustainability. Research efforts to quantify these impacts underline the necessity of multidisciplinary approaches and evidence-based strategies to mitigate their effects. This Research Topic emphasized the need to advance our understanding of the collective burden of animal diseases through a mix of frameworks, case studies, and policy-oriented analyses.The socioeconomic burden of disease encompasses financial costs, mortality, morbidity, and broader societal impacts. For animal diseases, this burden has predominantly been estimated using economic models focused on monetary costs. However, such models fail to account for the significant non-monetary burden of diseases, particularly in regions like sub-Saharan Africa, where livestock's social value often outweighs its economic value. Livestock provides resource-poor communities with food (milk, eggs and meat), agricultural benefits (draught power and manure), wealth storage, and cultural significance. When diseases cause livestock losses, the impact reverberates across all societal levels, requiring both direct costs (market-based) and indirect costs (non-monetary) to be estimated accurately.While direct costs can generally be quantified through market prices, indirect costs such as loss of cultural value, community status, and long-term social impacts are harder to estimate but often more consequential. These require robust mathematical and non-mathematical models for better assessment.Despite livestock’s immense societal value, limited literature exists on metrics for estimating the non-monetary burden of livestock diseases in developing regions. Some efforts, like modifying Disability-adjusted Life Years (DALYs) into zDALYs, attempt to monetize the non-monetary burden using time trade-offs (Torgerson et al., 2018). However, such approaches have been applied primarily to zoonotic diseases that impact both humans and animals, making time trade-offs feasible. These methods remain unexplored for non-zoonotic diseases, such as East Coast fever and Contagious Bovine Pleuropneumonia, which are prevalent in sub-Saharan Africa and cause substantial societal impacts.Keywords: Societal Burden of Animal Diseases, Socioeconomic Impact of Livestock Diseases, One Health Approach, Disease Burden Estimation, Animal Health Policy and Interventions. Key research contributionsQuantifying and Managing Uncertainty: One of the key contributions to this Research Topic was the development of robust frameworks for quantifying and managing uncertainty in animal disease burden estimation. Clough et al., (2025) presented an analytical framework that emphasizes transparency in documenting assumptions, ranking data quality, and conducting uncertainty and sensitivity analyses. Their approach underscored the importance of acknowledging uncertainty as an integral part of the decision-making process rather than viewing it as a limitation. The proposed stepwise methodology offers a replicable model for improving the reliability of disease burden estimates and fostering stakeholder confidence in the results.A Multisectoral Perspective: building on the need for a comprehensive understanding of animal disease impacts, Lysholm et al., (2025) introduced a framework for evaluating the multisectoral burden of animal diseases by integrating the impacts on animal health, human health, and the environment. Their framework aligns with the "One Health" paradigm. This holistic perspective is essential for identifying interventions that maximize societal benefits while addressing the interconnectedness of health outcomes across different sectors. The authors also highlighted the role of social cost-benefit analysis in prioritizing investments and policy decisions that account for both direct and indirect impacts of animal diseases.Localized Case Studies: The case studies featured in this Research Topic provide valuable insights into the localized impacts of animal diseases and the effectiveness of targeted interventions. Cai et al., (2023) examined the economic benefits of echinococcosis control measures in Qinghai Province, China. Their findings demonstrated the significant reductions in infection rates and economic losses achieved through dog deworming, lamb vaccination, and public education initiatives. Similarly, Kerfua et al., (2023) investigated the household-level effects of foot-and-mouth disease (FMD) in Uganda and Tanzania, revealing how market stabilization strategies and diversified livelihoods can mitigate the adverse impacts of disease outbreaks on vulnerable communities.Oba et al., (2023) focused on the economic losses associated with respiratory and helminth infections in domestic pigs in Lira district, Northern Uganda. Their study emphasized how improving farm management practices can significantly mitigate these losses, highlighting the interplay between management standards and infection control.Zhang et al., (2022) provided a cost and revenue analysis of porcine reproductive and respiratory syndrome (PRRS) outbreaks in Chinese pig farms. They quantified the extensive economic losses caused by the disease, emphasizing the importance of effective PRRS control strategies to mitigate its impact on pig production systems.Bessell et al., (2023) presented a high-level estimation of the net economic benefits to small-scale livestock producers arising from animal health product distribution initiatives, focusing on interventions in Africa and South Asia. Their findings underscored the transformative potential of veterinary pharmaceutical interventions in improving livelihoods and reducing disease burdens among resource-poor communities.Adoption of Disease Control Practices: Understanding the drivers and barriers to adopting disease control practices is crucial for improving implementation and compliance. Buchan et al., (2023) provided a comprehensive review of producer perceptions regarding disease control and welfare practices in the dairy and beef industries. Their findings highlighted the influence of financial constraints, knowledge gaps, and stakeholder attitudes on the adoption of biosecurity measures and vaccination programs.ConclusionThis research topic underscored the urgent need for holistic approaches to address the global burden of animal diseases. The diverse methodologies and case studies presented highlighted the critical intersection of science, policy, and practice in tackling these complex challenges by emphasizing the economic, social, and environmental dimensions of animal disease burdens. These contributions lay a foundation for evidence-based interventions that promote resilience and sustainability in livestock systems. Future DirectionsThe contributions to this Research Topic collectively pointed out the importance of integrating data-driven approaches, stakeholder engagement, and policy alignment to address the global burden of animal diseases. Moving forward, several priorities emerge:1.Enhancing Data Systems: Investments in data collection, integration, and accessibility are critical for improving the accuracy and reliability of burden estimates.2.Strengthening Collaboration: Multisectoral partnerships are essential for addressing the interconnected challenges of animal, human, and environmental health.3.Promoting Equity: Efforts to mitigate the burden of animal diseases must prioritize the needs of marginalized and livestock-dependent communities.4.Fostering Innovation: Sustainable and context-specific solutions are needed to balance economic, social, and environmental objectives.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.040
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.002
Science and technology studies0.0020.003
Scholarly communication0.0060.004
Open science0.0040.001
Research integrity0.0110.014
Insufficient payload (model declined to judge)0.0180.012

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.267
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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