An economic simulation was carried out over 183 milk-producing countries to estimate the global economic impacts of 12 dairy cattle diseases and health conditions: mastitis (subclinical and clinical), lameness, paratuberculosis (Johne's disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Estimates of disease impacts on milk yield, fertility, and culling were collected from the literature, standardized, meta-analyzed using a variety of methods ranging from simple averaging to random-effects models, and adjusted for comorbidities to prevent overestimation. These comorbidity-adjusted disease impacts were then combined with a set of country-level estimates for lactational incidence or prevalence or both, herd characteristics, and price estimates within a series of Monte Carlo simulations that estimated and valued the economic losses due to these diseases. It was estimated that total annual global losses are US$65 billion (B). Subclinical ketosis, clinical mastitis, and subclinical mastitis were the costliest diseases modeled, resulting in mean annual global losses of approximately US$18B, US$13B, and US$9B, respectively. Estimated global annual losses due to clinical ketosis, displaced abomasum, dystocia, lameness, metritis, milk fever, ovarian cysts, paratuberculosis, and retained placenta were estimated to be US$0.2B, US$0.6B, US$0.6B, US$6B, US$5B, US$0.6B, US$4B, US$4B, and US$3B, respectively. Without adjustment for comorbidities, when statistical associations between diseases were disregarded, mean aggregate global losses would have been overestimated by 45%. Although annual losses were greatest in India (US$12B), the United States (US$8B), and China (US$5B), depending on the measure of losses used (losses as a percentage of gross domestic product, losses per capita, losses as a percentage of gross milk revenue), the relative economic burden of these dairy cattle diseases across countries varied markedly.

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 pound 404/cow/year, equivalent to herd level losses of pound 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.

Indirect costs of animal disease outbreaks often significantly exceed the direct costs. Despite their importance, indirect costs remain poorly characterized due to their complexity. In this study, we developed a framework to assess the indirect costs of a hypothetical African swine fever outbreak in Switzerland. We collected data through international and national stakeholder interviews, analysis of national disease control regulations and industry data. We developed a framework to capture the resulting qualitative and quantitative data, categorize the impacts of these regulations, and rank the impacts in order of importance. We then developed a spreadsheet model to calculate the indirect costs of one category of control measure for an individual group of stakeholders. We developed a decision tree model to guide the most economically favourable implementation plan for a given control measure category, under different outbreak scenarios. Our results suggest that the most important measure/impact categories were 'Transport logistics', 'Consumer demand', 'Prevention of wild boar and domestic pig contact' and 'Slaughter logistics'. In our hypothetical scenario, the greatest costs associated with 'Prevention of wild boar and domestic pig contact' were due to assumed partial or total depopulation of fattening pig farms in order to reduce herd size to comply with the simulated control regulations. The model also provides suggestions on the most economically favourable strategy to reduce contact between wild boar and domestic pigs in control areas. Our approach provides a new framework to integrate qualitative and quantitative data to guide disease control strategy. This method could be useful in other countries and for other diseases, including in data- and resource-poor settings, or areas with limited experience of animal disease outbreaks.

Indirect costs of animal disease outbreaks often significantly exceed the direct costs. Despite their importance, indirect costs remain poorly characterised due to their complexity. In this study, we developed a framework to assess the indirect costs of a hypothetical African Swine Fever outbreak in Switzerland. We collected data through international and national stakeholder interviews, analysis of national disease control regulations and industry data. We developed a framework to capture the resulting qualitative and quantitative data, categorise the impacts of these regulations, and rank the impacts in order of importance. We then developed a spreadsheet model to calculate the indirect costs of one category of control measure for an individual group of stakeholders. We developed a decision tree model to guide the most economically favourable implementation plan for a given control measure category, under different outbreak scenarios. Our results suggest that the most important measure/impact categories were ‘Transport logistics’, ‘Consumer demand’, ‘Prevention of wild boar and domestic pig contact’ and ‘Slaughter logistics’. In our hypothetical scenario, the greatest costs associated with ‘Prevention of wild boar and domestic pig contact’ were due to assumed partial or total depopulation of pig farms in order to reduce herd size to comply with the simulated control regulations. The model also provides suggestions on the most economically favourable strategy to reduce contact between wild boar and domestic pigs in control areas depending on the duration of the outbreak. Our approach provides a new framework to integrate qualitative and quantitative data to guide disease control strategy. This method could be useful in other countries and for other diseases, including in data- and resource-poor settings, or areas with limited experience of animal disease outbreaks.

Various European Member States have implemented control or eradication programmes for endemic infectious diseases in cattle. The design of these programmes varies between countries and therefore comparison of the outputs of different control programmes is complex. Although output-based methods to estimate the confidence of freedom resulting from these programmes are under development, as yet there is no practical modeling framework applicable to a variety of infectious diseases. Therefore, a data collection tool was developed to evaluate data availability and quality and to collect actual input data required for such a modeling framework. The aim of the current paper is to present the key learnings from the process of the development of this data collection tool. The data collection tool was developed by experts from two international projects: STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, www.stocfree.eu) and SOUND control (Standardizing OUtput-based surveillance to control Non-regulated Diseases of cattle in the EU, www.sound-control.eu). Initially a data collection tool was developed for assessment of freedom of bovine viral diarrhea virus in six Western European countries. This tool was then further generalized to enable inclusion of data for other cattle diseases i.e., infectious bovine rhinotracheitis and Johne's disease. Subsequently, the tool was pilot-tested by a Western and Eastern European country, discussed with animal health experts from 32 different European countries and further developed for use throughout Europe. The developed online data collection tool includes a wide range of variables that could reasonably influence confidence of freedom, including those relating to cattle demographics, risk factors for introduction and characteristics of disease control programmes. Our results highlight the fact that data requirements for different cattle diseases can be generalized and easily included in a data collection tool. However, there are large differences in data availability and comparability across European countries, presenting challenges to the development of a standardized data collection tool and modeling framework. These key learnings are important for development of any generic data collection tool for animal disease control purposes. Further, the results can facilitate development of output-based modeling frameworks that aim to calculate confidence of freedom from disease.

Transboundary and Emerging DiseasesVolume 67, Issue 3 p. 1043-1044 LETTER TO THE EDITOR The ongoing crises in China illustrate that the assessment of epidemics in isolation is no longer sufficient Carla Stoffel, Corresponding Author Carla Stoffel carla.stoffel@safoso.ch orcid.org/0000-0002-9210-3458 SAFOSO AG, Liebefeld, Switzerland Correspondence Carla Stoffel, SAFOSO AG, Waldeggstrasse 1, CH 3097 Liebefeld, Switzerland. Email: carla.stoffel@safoso.chSearch for more papers by this authorManon Schuppers, Manon Schuppers SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorPatrik Buholzer, Patrik Buholzer orcid.org/0000-0002-9758-2507 SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorVioleta Muñoz, Violeta Muñoz SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorIsabel Lechner, Isabel Lechner orcid.org/0000-0001-5155-9734 SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorUlrich Sperling, Ulrich Sperling SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorSusanne Küker, Susanne Küker SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorMarco De Nardi, Marco De Nardi orcid.org/0000-0001-5648-257X SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this author Carla Stoffel, Corresponding Author Carla Stoffel carla.stoffel@safoso.ch orcid.org/0000-0002-9210-3458 SAFOSO AG, Liebefeld, Switzerland Correspondence Carla Stoffel, SAFOSO AG, Waldeggstrasse 1, CH 3097 Liebefeld, Switzerland. Email: carla.stoffel@safoso.chSearch for more papers by this authorManon Schuppers, Manon Schuppers SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorPatrik Buholzer, Patrik Buholzer orcid.org/0000-0002-9758-2507 SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorVioleta Muñoz, Violeta Muñoz SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorIsabel Lechner, Isabel Lechner orcid.org/0000-0001-5155-9734 SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorUlrich Sperling, Ulrich Sperling SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorSusanne Küker, Susanne Küker SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this authorMarco De Nardi, Marco De Nardi orcid.org/0000-0001-5648-257X SAFOSO AG, Liebefeld, SwitzerlandSearch for more papers by this author First published: 07 March 2020 https://doi.org/10.1111/tbed.13536Citations: 6Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume67, Issue3May 2020Pages 1043-1044 This article also appears in:Novel coronavirus 2019 − an emerging public health emergency RelatedInformation

Within the European Union (EU), microbiological criteria (MC) sampling for Salmonella in poultry was introduced in 2005. In particular, processors had to meet a target of fewer than seven positive samples out of 50. However, processors producing small amounts of poultry meat did not have to sample if national authorities determined this was an acceptable risk. The U.K. Food Standards Agency (FSA) thus has a sampling regime based on throughput that allows smaller processors not to sample. In 2011, the limit of 7/50 was reduced to 5/50. Given the current uncertainty regarding U.K. trade relations with the EU, the U.K. FSA decided to conduct a new risk assessment of the risks of Salmonella produced by smaller processors, to determine whether sampling was now necessary. Current evidence suggests that an MC sampling regime in smaller slaughterhouses is not warranted from a national public health perspective. Because of the insensitivities of the MC sampling scheme, the introduction of MC sampling into smaller slaughterhouses would only be necessary if the suspected carcass prevalence was 15% or more. While our analysis is prone to uncertainty, we estimated that the carcass prevalence in smaller processors is below this. Thus, we recommended that the current sampling framework, allowing smaller processors not to sample, was still applicable.