D.Mayberry^1,2, Y.Li^1,2, P.Schrobback^1,2, G.Dennis^1,2, C.Fischer^1,3, M.Herrero^1,3

¹ Global Burden of Animal Diseases programme

²CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Australia

³Department of Global Development, College of Agriculture and Life Sciences, and Cornell Atkinson Centre for Sustainability, Cornell University, Ithaca, USA

As part of the Global Burden of Animal Diseases (GBADs) Program, the Populations and Production Systems Theme is collating and analysing data to quantify the biomass and economic value of terrestrial livestock production at local and global scales. This information underpins analyses by other GBADs Themes to understand the magnitude and value of animal health losses within a system, and how changes in livestock populations might impact livelihoods and the environment.
Biomass is the sum of individual liveweights for a given population. At the global scale, this can be calculated using country-level estimates of livestock populations (e.g., from FAOSTAT) and liveweight values based on either generic species-level assumptions (e.g., Tropical Livestock Units) or proxy data (e.g., average slaughter weight). More accurate estimates of biomass are available at national and sub-national scales in countries where data is available to disaggregate populations by breed, sex, and age structure, and more appropriate liveweight values can be assigned to each class of livestock. The economic value of livestock production was calculated at the global scale as the combination of asset values (i.e., live animals) and output values (e.g., meat, eggs, milk).
At the global scale, while poultry have the largest population (25.4 billion head, 38.8 billion kg liveweight, 444 billion USD in 2018), biomass and market value are dominated by the cattle sector (1.5 billion head, 483.7 billion kg liveweight, 1.79 trillion USD in 2018). The comparison of asset and output values for livestock across different species and countries highlights differences in the ways livestock are valued. For example, results suggest that poultry and pigs are mostly valued for the output they produce (meat and eggs), while cattle and small ruminants have similar asset and production values. The relative contribution of asset value to total market value is highest in low- and middle-income countries, reflecting the varying and complex socio-economic roles of livestock around the world.

W.Gilbert^1,2, G.Chaters^1,2 and J.Rushton^1,2

¹ Global Burden of Animal Diseases programme

² Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool L3 5RF, United Kingdom

The efficiency of livestock systems is compromised by communicable and non-communicable disease, accidents, injuries, predation, drought and malnutrition. The Global Burden of Animal Diseases programme (GBADs) recognises the economic purpose that livestock fulfil and quantifies the burden of these hazards at farm level in financial terms in the first instance by means of an animal health loss envelope (AHLE). The defining characteristics of this envelope are intended to exclude productivity change arising from non-health effects such as genetic and technological variation, and assess system productivity against an ideal free from morbidity and mortality. In such a manner, all causes of both morbidity and mortality theoretically can be attributed within this envelope with no risk of double counting. This short communication discusses the economic basis for setting the boundaries of the envelope, the progress made within the GBADs programme toward operationalising the AHLE concept and the plans for its further development in terms of data availability and analytical methods.

S.Kwok^1,2, A.Larkins^1,2, Y.Li1,3, T.Knight-Jones^1,4, W.T.Jemberu^1,4, G.Chaters^1,5 and M.Bruce^1,2

¹Global Burden of Animal Diseases programme

²School of Veterinary Medicine, Centre for Biosecurity and One Health, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia

³CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Australia

⁴International Livestock Research Institute, Addis Ababa, Ethiopia

⁵Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool L3 5RF, United Kingdom

Digital technologies in agricultural infrastructure and research produces large quantities of data that will facilitate understanding of animal production systems and measuring the burden of disease. Due to the heterogeneity within these sources, a machine-readable data model with a well-defined vocabulary of terms, connected with logical relationships – an ontology – is required. The design of the Animal Health Ontology (AHO) as a is informed by its application for the Global Burden of Animal Diseases program. It includes components for measuring the burden of disease; (i) animal demography (species, breed, sex, age); (ii) production system categories; (iii) the inputs and outputs of the production systems; and (iv) disease characteristics (Figure 1). Each term within the AHO has a definition, synonyms, context for use, and links to other animal health data catalogues. The AHO is central for data integration, enabling the annotation of data sources, such as census data, published studies and laboratory reports.

The ontology-based data integration approach is used in the methodological framework for the cause-specific attribution of the animal health loss envelope. The starting point is a cause list, broadly categorised into infectious, non-infectious, and external factors. For each cause, characteristics including measures of disease frequency, severity, duration, and outcome – expressed as a loss in production for example milk, meat, or fertility, or mortality – are defined. The accurate identification and specification of disease characteristics within the AHO provides a data-driven approach to assist in the annotation of heterogenous animal health data required to measure the burden of disease. These annotated datasets are then machine-readable, discoverable in the semantic web, interoperable and reusable. The cause list, associated characteristics and outcomes will be updated and expanded as new data becomes available, whilst adjusting for multimorbidity will improve these estimates.

P.Rasmussen^1,2, A.P.M. Shaw³, V.Muñoz^1,2, M.Bruce^1,4 and P.R.Torgerson^1,2

¹Global Burden of Animal Diseases programme

²Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 270, 8057 Zürich, Switzerland

³AP Consultants, 22 Walworth Enterprise Centre, Duke Close, Andover SP10 5AP, United Kingdom

⁴School of Veterinary Medicine, Centre for Biosecurity and One Health, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia

The Global Burden of Animal Diseases (GBADs) is an international collaboration aiming, in part, to measure and improve societal outcomes from livestock. One GBADs objective is to estimate the economic impact of endemic diseases in livestock. However, if individual disease impact estimates are linearly aggregated without consideration for associations among diseases, there is the potential to double count impacts, overestimating the total burden. Accordingly, the authors propose a method to adjust an array of individual disease impact estimates so that they may be aggregated without overlap.

Using Bayes’ Theorem, conditional probabilities were derived from inter-disease odds ratios in the literature. These conditional probabilities were used to calculate the excess probability of disease among animals with associated conditions, or the probability of disease overlap given the odds of coinfection, which were then used to adjust disease impact estimates so that they may be aggregated. The aggregate impacts, or the yield, fertility, and mortality gaps due to disease, were then attributed and valued, generating disease-specific losses. The approach was illustrated using an example dairy cattle system with input values and supporting parameters from the UK, with 13 diseases and health conditions endemic to UK dairy cattle: cystic ovary, disease caused by gastrointestinal nematodes, displaced abomasum, dystocia, fasciolosis, lameness, mastitis, metritis, milk fever, neosporosis, paratuberculosis, retained placenta, and subclinical ketosis.

The diseases and conditions modelled resulted in total adjusted losses of £ 404/cow/year, equivalent to herd-level losses of £ 60,000/year. Unadjusted aggregation methods suggested losses 14-61% greater. Although lameness was identified as the costliest condition (28% of total losses), variations in the prevalence of fasciolosis, neosporosis, and paratuberculosis (only a combined 22% of total losses) were nearly as impactful individually as variations in the prevalence of lameness. The results suggest that from a disease control policy perspective, the costliness of a disease may not always be the best indicator of the investment its control warrants; the costliness rankings varied across approaches and total losses were found to be surprisingly sensitive to variations in the prevalence of relatively uncostly diseases.

This approach allows for disease impact estimates to be aggregated without double counting. It can be applied to any livestock system in any region with any set of endemic diseases, and can be updated as new prevalence, impact, and disease association data become available. This approach also provides researchers and policymakers an alternative tool to rank prevention priorities.

D.L. Pendell^1,2, A. Kappes^1,3, T.Tozooneyi^1,2, T.L.Marsh^1,3

¹Global Burden of Animal Diseases programme

² Department of Agricultural Economics, Kansas State University, Manhattan, Kansas 66506, United States of America

³ Paul G. Allen School for Global Animal Health, Allen Center, School of Economic Sciences, Washington State University, P.O. Box 647090, 1155 College Avenue, Pullman, Washington 99164, United States of America

Increasing population growth and rising demand for livestock and livestock products are integral to improving food and nutrition security, health, and livelihoods across the world. As such, interest has grown in identifying and assessing the wider economic burdens of animal health and livestock diseases to better understand and prioritize resource allocation. Who is burdened and by how much? Economic methods and empirical measures will be presented with selected case studies in small ruminants in Ethiopia and broilers in the United States.

Methods:  Methods will follow and expand on the economic literature, which rely on equilibrium calibration models to assess market outcomes for animal health and livestock disease events (Paarlberg et al. 2008; Pendell et al. 2015, 2016; Tozer et al. 2012; Nogueria et al. 2011; Hennessy and Marsh 2021). Measures of consumer surplus and producer surplus will be used to assess the amount of economic burden across economic agents. Currently, we are focusing on broilers in the USA. In the United States, agriculture and livestock are important but a small part of the GDP, so a partial equilibrium model is employed. We are also focusing on small ruminants in Ethiopia. Here, agriculture and livestock play a major role in the country’s GDP, so both a partial equilibrium and a general equilibrium model are used.

Case Studies:  Specifically for broilers in the USA the approach will follow Thompson et al. (2019). For small ruminants in Ethiopia, the methods are defined in Golam (2022).

Brecht Devleesschauwer^1,2,6, Carlotta Di Bari^1,2, Narmada Venkateswaran^1,3, Christina Fastl^1,2, Grace Patterson^1,4, Andrew Larkins^1,5, and David Pigott^1,3

¹Global burden of Animal Diseases programme

²Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium

³Institute for Health Metrics and Evaluation, Department of Health Metrics Sciences, University of Washington, Seattle, WA, United States

⁴ Department of Population Medicine, Ontario Veterinary College, University of Guelph, 50 Stone Road E., Guelph, Ontario N1G 2W1, Canada

⁵ School of Veterinary Medicine, Centre for Biosecurity and One Health, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia

⁶Department of Translational Physiology, Infectiology, and Public Health, Ghent University, Merelbeke, Belgium

Livestock are an essential component of human society, whether as a source of food, an asset, or a tradable commodity generator. As such, poor health outcomes in animals have a variety of impacts on subsequent human welfare and health, directly and indirectly. Using the Disability-Adjusted Life Years (DALYs) framework, we have explored the linkages between human and animal health outcomes, and identified necessary data and proposed methodological pathways to quantify these outcomes, with more detailed examples outlined for zoonotic pathogens, anti-microbial resistance (AMR), and diet.

For zoonotic pathogens, we identified global priorities for zoonotic pathogens, demonstrating where burden assessments are most necessary and surveyed the literature to identify the current state-of-the-art with respect to regional and global estimates of their impact. While some pathogens, such as Salmonella, are represented often, this is more through the lens of food safety, or diarrheal disease, rather than zoonoses. In contrast, a zoonotic disease such as anthrax, which has both biosecurity concerns in addition to transmission in endemic settings, was identified as a priority by 73 different nations, yet no global burden assessment has been undertaken. For brucellosis, another priority zoonosis, we undertook a detailed review of prior burden estimates to identify key data gaps and parameters that are disproportionately overlooked in current assessments. For instance, we saw variation in the sequelae associated with brucellosis infection, paired with duration parameters ranging from two weeks to four and a half years; such decisions have large impacts on subsequent total DALYs.

For AMR, we are determining the One Health priority drugs, and evaluating global data on AMR prevalence in livestock species by assessing the ResistanceBank repository and selected national survey programs. We identify countries and bug-drug combos that are currently underrepresented within existing surveillance and contrast these gaps with key features such as livestock census counts to better understand the possible consequences of this missing data. Moreover, we evaluate the correlation between the percentage of isolates from humans that were resistant for specific bug-drug combinations, as estimated by the Global Burden of Disease, with their animal comparator to determine which combinations are the most likely to benefit from more detailed investigations into causal linkages between animal patterns of resistance and human infections. To further explore the available evidence on this link, we review and summarize studies attributing human AMR to livestock AMR.

Finally, we lay out a holistic blueprint for quantifying the total impact of poor animal health on humans, and using diet and nutrition as an example, we outline a quantification pathway by which forecasts, and scenarios could be produced that propagate changes in the health of livestock and their subsequent impacts, mediated via food, on human health outcomes. We identify a constellation of pre-existing processes that, if chained together, could translate measures of productivity losses or gains, into macro- and micronutrient intake patterns, changes in population risks and exposures, and therefore corresponding changes in human health. In doing so, we demonstrate a proof-of-concept as to how we can begin to breakdown the various consequences of poor animal health into quantifiable and comparable elements. These collated methods and data represent the first time that a comprehensive attempt to quantify the total impact of animal health on humans has been presented.

T.M. Bernardo^1,3, D.A. Stacey^1,2, G.T. Patterson^1,3, K. Raymond^1,2, N. BenSassi^1,3, L.Nguyen^1,2,

 ¹Global Burden of Animal Diseases programme

²School of Computer Science, University of Guelph, Guelph, ON N1G 2W1 Canada

³Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1 Canada

The calculation of the Global Burden of Animal Diseases (GBADs) will be based on the most recent data-driven evidence accessed from existing data sources.  Whereas the Global Burden of Disease (GBD) Study for humans, first published in 1990, is now updated every few years, the goal for GBADs is to provide continual updating as changes are made to the underlying data.  Providing transparency regarding where the data came from and what has been done to it, as well as an assessment of data quality, will engender trust in the data itself, as well as the resulting models and dashboards. We are also incorporating best practices and standards, as documented in our Data Governance Handbook.

Even an apparently simple input variable, such as the number of animals by species, has proved difficult to reconcile as each species is categorized in different manners by leading data sources and over time.  Each output serves as an input for additional calculations, introducing the risk of amplifying data errors.  For example, accurate estimates of livestock numbers are necessary to estimate biomass, which is used in estimating economic investment in animals and infrastructure for GBADs.  Biomass, however, is also used to estimate antimicrobial use and for climate change deliberations, illustrating the importance of sharing reliable data across human, animal, and environmental health, or One Health Data.  GBADs data also factor into Sustainable Development Goals, including hunger, health, responsible production, climate action and livelihoods.

The Informatics Theme, comprised of a mix of epidemiologists and computer, data and other scientists at the University of Guelph, works closely with other GBADs Themes to ensure reproducible results, facilitate data flow, support data analysis and visual representation in dashboards and through data stories. An overview of our data products will be presented.

Wudu Temesgen^1,2, Gemma Chaters^1,3, Theodore Knight-Jones^1,2, Peggy Schrobback^1,4, Li Yin^1,4, Andrew Larkins^1,5 and Mieghan Bruce^1,5

¹Global Burden of Animal Diseases programme

²International Livestock Research Institute, Addis Ababa, Ethiopia

³Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool L3 5RF, United Kingdom

⁴CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Australia

⁵School of Veterinary Medicine, Centre for Biosecurity and One Health, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia

The Ethiopian case study provides a worked example of the GBADs process and methodology from data gleaning, cleaning and processing to outputs. National central statistical agency databases, meta-analysis of literature review data and expert elicitation (Cooke and IDEA methods) were used to generate data for the analytical process (Fig 1). Key livestock species relied upon in Ethiopia include cattle, poultry, equids, camels and small ruminants and here we present the small ruminant example.

Step 1 classified livestock production systems present in the country 

Step 2 estimated livestock biomass within each production system (number of animals multiplied by average weight of animals within age-sex groups). 

Step 3 estimated the monetary value of livestock (number of animals multiplied by current market value) and the economic output value (offtake, change in flock size, hides, dung, milk) by multiplying current production volume (e.g., heads, kg, litre) by corresponding market prices. 

Step 4 estimated the animal health loss envelope (AHLE). This is the difference in production (outputs – inputs) between the current scenario and an idealised scenario where all livestock are free from disease, accidents, injury and predation and have adequate access to water and nutrition. For sheep in the crop mixed livestock system the AHLE was estimated at 30500 million birr (US$ 735 M) per year. 

Step 5 attributed the AHLE to three broad level causes: i) external factors ii) infectious causes and iii) non-infectious causes. Initial analysis shows 49% (median) of mortality losses in small ruminants in the crop-mixed-livestock system (10 million animals per year) are attributable to infectious causes, 28% to non-infectious and 23% to external.

Step 6 passes the AHLE to the wider economy theme who will estimate gross changes and impact on the economy resulting from the AHLE burden and Step 7 is to work with the human health theme to estimate a combined burden of zoonoses on human and livestock health.