The Global Burden of Animal Diseases (GBADs) programme gave a Special Session presentation at ISVEE 16. Members of the GBADs team gave multiple talks on the work each theme are working on and how each theme links together. You can find the abstracts from our presentations below.

The current use of economics in animal health tends to focus on specific diseases, largely the transboundary diseases, and a limited range of strategies to control these diseases. This partial approach leaves the animal health profession poorly placed to understand where burdens lie at farm, sector and societal level. The Global Burden of Animal Diseases programme will provide a powerful basis for evidence-based decision-making in animal health, livestock production and the livestock sector in general. It will achieve this through presenting the animal health burden in standardised terms of its economic components: production loss; expenditure; and trade. The GBADs information portal will allow users to examine this burden: by the type of farmer and consumer; in different geographical regions; and in different time periods.

The session will report on the progress of the GBADs programme in terms of methods, information at global, regional and country level and education.


J.Rushton1,2, W.Gilbert1,2, G.Chaters1,2, K.M.McIntyre1,2 and B.Huntington1,2,4

1Global Burden of Animal Diseases programme

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

3World Organisation for Animal Health, 12 rue de Prony 75017, Paris, France

4Pengwern Animal Health Ltd, 259 Wallasey Village, Wallasey Wirral, Merseyside CH45 3LR, United Kingdom

The Global Burden of Animal Diseases (GBADs) programme is a consortium of research and academic organisations co-led by the World Organisation of Animal Health (WOAH) and the University of Liverpool. GBADs is developing and implementing a systematic assessment of the burden of animal diseases and health problems.

Figure 1: GBADs Overarching Framework

GBADs has developed an overarching framework (see Figure). Livestock populations are split by species and where possible production systems with information generated on their biomass and the economic investment in animals. This is followed by an assessment of the gap in production due to disease and health issues, which has been called an animal health loss envelope (AHLE) that is made up of production loss and expenditure. The AHLE creates a boundary for the total burden for a livestock population and it can be attributed by cause and where possible risk factors. The assessments of the burden of diseases at farm-level will be used in sector and economy models to determine who in society is impacted by the burden. The information of the animal disease burden is also being explored in terms of impacts on human health with regards to zoonoses, AMR and nutrition. Data to generate the burden estimates and their impacts are gleaned from the academic literature, and public and private organisations. GBADs is working at a global level with country case studies.

The GBADs programme is in the proof-of-concept phase and is making early estimations of disease burdens at a global and national levels. The details of the data flows and analytic procedures will be outlined in the special session by the GBADs theme leads.


Huntington, B., Bernardo, T.M., Bruce, M., Devleesschauwer, B., Gilbert, W., Havelaar, A., Herrero, M., Marsh, T.L., Mesenhowski, S., Pendell, D., Pigott, D., Grace Randolph, D., Bondad-Reantaso, M., Shaw, APM, Stacey, D., Stone, M., Torgerson, P., Watkins, K., Wieland, B., & Rushton, J. (2021) Global Burden of Animal Disease: a novel approach to understanding and managing disease in livestock and aquaculture. Why is it needed, what is its theoretical basis and what will it contribute? OIE Rev Tech Sci 40 (2) pp 567-584

J Rushton, B Huntington, W Gilbert, M Herrero, P R Torgerson, A P M Shaw, M Bruce, T L Marsh, D L Pendell, T M Bernardo, D Stacey, D Grace, K Watkins, M Bondad-Reantaso, B Devleesschauwer, D M Pigott, M Stone, S Mesenhowski (2021) Roll-out of the Global Burden of Animal Diseases programme  The Lancet Published: February 04, 2021 Rushton, J., Bruce, M., Bellet, C., Torgerson, P., Shaw, A.P.M., Marsh, T.L., Pigott, D., Stone, M., Pinto, J., Mesenhowski, S., Wood, P. (2018) Initiation of the Global Burden of Animal Diseases (GBADs) Program. The Lancet Vol 392 pp 538-54


D.Mayberry1,2, Y.Li1,2, P.Schrobback1,2, G.Dennis1,2, C.Fischer1,3, M.Herrero1,3

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.Gilbert1,2, G.Chaters1,2 and J.Rushton1,2

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.Kwok1,2, A.Larkins1,2, Y.Li1,3, T.Knight-Jones1,4, W.T.Jemberu1,4, G.Chaters1,5 and M.Bruce1,2

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

Figure 1. Schematic representation of the Animal Health Ontology, illustrating the broad concepts (orange boxes), sub-concepts (white boxes with orange border) and their relationships (arrows). The AHO integrates with data and terminology across themes within the Global Burden of Animal Diseases program (white ovals with green border). ETH: Ethiopian case study; PLE: Production loss envelope; PPS: Population and Production System; and WEI: Wider economic impacts themes


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.Rasmussen1,2, A.P.M. Shaw3, V.Muñoz1,2, M.Bruce1,4 and P.R.Torgerson1,2

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. Pendell1,2, A. Kappes1,3, T.Tozooneyi1,2, T.L.Marsh1,3

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 Devleesschauwer1,2,6, Carlotta Di Bari1,2, Narmada Venkateswaran1,3, Christina Fastl1,2, Grace Patterson1,4, Andrew Larkins1,5, and David Pigott1,3

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. Bernardo1,3, D.A. Stacey1,2, G.T. Patterson1,3, K. Raymond1,2, N. BenSassi1,3, L.Nguyen1,2,

 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 Temesgen1,2, Gemma Chaters1,3, Theodore Knight-Jones1,2, Peggy Schrobback1,4, Li Yin1,4, Andrew Larkins1,5 and Mieghan Bruce1,5

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.


M.Stone1,2, E.M.Kallon1,2, A.Pineau1,2, T.Shorten1,3, Y.M.Soko1,2 and B.Huntington1,4,5

1 Global Burden of Animal Diseases programme

2 World Organisation for Animal Health, 12 rue de Prony 75017, Paris, France

3 StanStar Consulting, Priory Farm, Half Moon Lane, Redgrave, Diss, Norfolk, England, IP22 1RX

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

5 Pengwern Animal Health Ltd, 259 Wallasey Village, Wallasey Wirral, Merseyside CH45 3LR, United Kingdom

The Global Burden of Animal Diseases (GBADs) established critical success factors within its monitoring and evaluation arrangements that include validation of methods, demonstrating their utility, promoting broad uptake and implementation by partners, and eventual global acceptance. These factors act in synergy since greater uptake of GBADs will expand data sources, ensuring more animal population and production systems are specifically covered, allowing further testing and refinement of methods, and increasing the accuracy of outputs in a virtuous cycle.

Validation is defined as broad acceptance of GBADs methods in the scientific, policy and international development community.

Institutionalisation is defined as: the incorporation of GBADs methodology into a new Chapter or Articles of the World Organisation for Animal Health (WOAH) Terrestrial and Aquatic Codes; embedding the GBADs methodology into WOAH programmes and global initiatives; building the internal capacity of WOAH Members and Partners (including international organisations, development banks, donors, and private sector organisations) and other entities to use the GBADs methodology; and incorporating outputs into the decision-making processes for investments in animal health.

This paper summarises how WOAH and the University of Liverpool plan to lead validation, institutionalisation and uptake of GBADs, addressing activities undertaken during Phase II of the programme (2021-2022), and those planned for the following phase (Phase III, 2023-2027).

Publication of methods and models in the scientific literature is a key element of validation. As well as external peer review managed through the editorial processes of scientific journals, the GBADs programme is designing and implementing expert review mechanisms prior to submission of publications. 

WOAH’s commitment to GBADs is clearly stated in the organisation’s strategy. As an international standard setting body, WOAH has developed processes, capability and expert networks that will be used to incorporate GBADs methods and approaches into standards and guidelines. In parallel, WOAH will ensure its programmes contribute to and use data and outputs from GBADs. WOAH’s capacity development programmes, in particular the PVS Pathway and the Training Platform, will develop capacity in Members, Partners (international organisations, development partners, and private sector associations), and the organisation’s network (WOAH Collaborating Centres, Veterinary Education Establishments) to use GBADs methods. WOAHs publications and conferences will promote GBADs and its outputs.

The GBADs programme is developing a stakeholder community that includes partners supporting development of methods (technical and financial support), those providing data through data alliances, and organisations that are intended users of GBADs products. Strategy and plans will guide prioritised and targeted engagement with all these groups during the next phase of the programme.

AD.Hagerman1, L.Alban2, L.Donnison3, K.Havas4, T.Kimani5, M.Mulumba6, W.Okelo7, Ugo Pica-Ciamarra8, Crawford Revie9, Tim Robinson10, Mo Salman11, Philip Thornton12

1 Oklahoma State University, Department of Agricultural Economics, 308 Agriculture Hall, Stillwater, OK 74078, USA
2 Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 8, DK-1870 Frederiksberg, Denmark
3 Supporting Evidence Based Interventions (SEBI) Royal (Dick) School of Veterinary Studies University of Edinburgh, UK
4 Pipestone, 1300 So Highway 75, Pipestone, Minnesota, 56164, USA
5 Food and Agriculture Organization of the United Nations, Nairobi, Kenya
6 Onderstepoort Veterinary Institute, Agricultural Research Council, Onderstepoort, Pretoria 0110, South Africa
7 Commonwealth Scientific and Industrial Research Organisation, Black Mountain Science and Innovation Park, Canberra, ACT, Australia
8 Food and Agriculture Organization of the United Nations, Italy
9 Computer and Information Sciences, University of Strathclyde, Glasgow, UK
10 Food and Agriculture Organization of the United Nations, FAO · Livestock Information, Sector Analysis and Policy Branch
11 Animal Population Health Institute, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, USA
12 CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Livestock Research Institute (ILRI), Nairobi, Kenya

A Reference Group (RG) consisting of subject matter scientists in livestock population modelling, epidemiology, economics, and data science was mid-2022 tasked to assess GBADs data flows, data sources, as well as proposed and used applied analytical tools. The RG listened to presentations by GBADs collaborators before filling in a pre-developed standard matrix for each of the presented topics that covered (1) relevance (2) technical clarity of methods (3) data and estimates and (4) overall approach for the Phase II work-to-date.

According to the RG, the GBADs project addresses an urgent need to collect and synthesize the relevant livestock health scattered data and information for stakeholders, including policy makers, so the limited available resources can be allocated appropriately for enhancing global animal health programs. The overall quality of the work in Phase II was recognized by the RG, as being well-structured and containing various components covering a required variety of disciplines to achieve GBADs’ mission and aims.

Although GBADs is progressing well, there are several tasks that should be considered to move the project from concept level to a finalized program that could have significant benefits to the global animal health community. Some of the suggestions in moving forward include, first, addressing the reliability of the required collected data and their resources. This includes expansion of the diversity of the resources of data in relation to geographic granularity to capture critical differences in production types, livestock production systems, climate conditions, and public and private animal health services structure. Second, GBADs should allow for flexibility in the framework to account for differences in data availability, diseases of concern, or regional characteristics while still providing comparable estimates for decision makers, Third, complexities and linkages that may be out of the scope of the project should be recognized. Finally, the cohesiveness of individual efforts should be increased.

With GBADs Phase III on the horizon, the program would benefit from the development of some key impact pathways to illustrate exactly how GBADs plans to achieve its aims. This also relates to the assumptions that underlie these pathways. The development of detailed impact pathways would also allow realistic target indicators to be developed, that can be monitored throughout the course of the program. This will also require a variety of denominators to be assessed for their applicability. Moreover, the success of the GBADs requires the acceptance of both the providers and users of such needed programs to support livestock production, global health, and food sustainability. Therefore, stakeholder communication should be prioritized.

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