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Enregistrement W4408226450 · doi:10.3389/fvets.2025.1571267

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

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

Notice bibliographique

RevueFrontiers in Veterinary Science · 2025
Typeeditorial
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAnimal Disease Management and Epidemiology
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésLivestockBurden of diseaseDisease managementNatural resource economicsDisease burdenDiseaseAgricultural economicsEconomicsMedicineBiologyEcologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,040
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,061

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,040
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0040,002
Études des sciences et des technologies0,0020,003
Communication savante0,0060,004
Science ouverte0,0040,001
Intégrité de la recherche0,0110,014
Charge utile insuffisante (le modèle a refusé de juger)0,0180,012

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,267
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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