Social and ethical implications of data and technology use on farms: a qualitative study of Swedish dairy and pig farmers.

Introduction: Livestock farmers are being increasingly encouraged to adopt digital health technologies on their farms. Digital innovations may have unintended consequences, but there tends to be a pro-innovation bias in previous literature. This has led to a movement towards "responsible innovation," an approach that questions the social and ethical challenges of research and innovation. This paper explores the social and ethical issues of data and technologies on Swedish dairy and pig farms from a critical perspective. Methods: Six focus groups were conducted with thirteen dairy and thirteen pig farmers. The data were analysed using reflexive thematic analysis and a digital critical health lens, which focuses on concepts of identity and power. Results and discussion: The analysis generated four themes: extending the self, sense of agency, quantifying animals, and managing human labour. The findings suggest that technologies can change and form the identities of farmers, their workers, and animals by increasing the visibility of behaviours and bodies through data collection. Technologies can also facilitate techniques of power such as conforming to norms, hierarchical surveillance, and segregation of populations based on data. There were many contradictions in the way that technology was used on farms which suggests that farmers cannot be dichotomised into those who are opposed to and those that support adoption of technologies. Emotions and morality played an important role in the way animals were managed and technologies were used by farmers. Thus, when developing innovations, we need to consider users' feelings and attachments towards the technologies. Technologies have different impacts on farmers and farm workers which suggests that we need to ensure that we understand the perspectives of multiple user groups when developing innovations, including those that might be least empowered.

Prioritisation of zoonotic diseases for coordinated surveillance systems under the One Health approach for cross-border pathogens that threaten the Union.

In the context of the initiative 'CP-g-22-04.01 Direct grants to Member States' authorities', EFSA was requested to develop and conduct a prioritisation of zoonotic diseases, in collaboration with Member States, to identify priorities for the establishment of a coordinated surveillance system under the One Health approach. The methodology developed by EFSA's Working Group on One Health surveillance was based on a combination of multi-criteria decision analysis and the Delphi method. It comprised the establishment of a list of zoonotic diseases, definition of pathogen- and surveillance-related criteria, weighing of those criteria, scoring of zoonotic diseases by Member States, calculation of summary scores, and ranking of the list of zoonotic diseases according to those scores. Results were presented at EU and country level. A prioritisation workshop was organised with the One Health subgroup of EFSA's Scientific Network for Risk Assessment in Animal Health and Welfare in November 2022 to discuss and agree on a final list of priorities for which specific surveillance strategies would be developed. Those 10 priorities were Crimean-Congo haemorrhagic fever, echinococcosis (both E. granulosus and E. multilocularis), hepatitis E, influenza (avian), influenza (swine), Lyme borreliosis, Q-fever, Rift Valley fever, tick-borne encephalitis and West Nile fever. 'Disease X' was not assessed in the same way as other zoonotic diseases on the list, but it was added to the final list of priorities due to its relevance and importance in the One Health context.

Coordinated surveillance system under the One Health approach for cross-border pathogens that threaten the Union - options for sustainable surveillance strategies for priority pathogens.

This report provides guidance for Member states who plan to submit applications under the work programme 'CP-g-22-04.01 Direct grants to Member States' authorities'. The priority pathogens on which the coordinated surveillance under the grant initiative shall focus have been identified in a prioritisation exercise with Member States and ECDC. These are Crimean Congo haemorrhagic fever, echinococcosis, hepatitis E, highly pathogenic avian influenza (HPAI), influenza in swine, Lyme disease, Q-fever, Rift Valley fever, tick-borne encephalitis, West Nile fever and Disease X (Disease Y of animals). Surveillance activities (surveillance cards) have been proposed for these agents in this report. Member States should select one or more diseases from the list of priority diseases and then choose surveillance activities from the surveillance cards and modify them where needed, to reflect their national needs and situation. Member States can also design alternative surveillance activities for the priority infectious agents that may better fit the epidemiological situation in their country. Further, this report provides a section on surveillance perspectives that links infectious agents to different hosts, allowing Member States to consider the testing for multiple infectious agents in samples from a single host population, as well as sections providing guidance on surveillance in vectors and wildlife and for Disease X (Disease Y in animals). Member States are encouraged to develop cross-sectoral collaborations and the report provides guidance on cross-sectoral collaboration to help them. Finally, there is a roadmap providing an overall description of the steps in the process of developing a surveillance system in order to apply for the grant.

Data-fed, needs-driven: Designing analytical workflows fit for disease surveillance.

Syndromic surveillance has been an important driver for the incorporation of "big data analytics" into animal disease surveillance systems over the past decade. As the range of data sources to which automated data digitalization can be applied continues to grow, we discuss how to move beyond questions around the means to handle volume, variety and velocity, so as to ensure that the information generated is fit for disease surveillance purposes. We make the case that the value of data-driven surveillance depends on a "needs-driven" design approach to data digitalization and information delivery and highlight some of the current challenges and research frontiers in syndromic surveillance.

Data workflows and visualization in support of surveillance practice.

The Swedish National Veterinary Institute (SVA) is working on implementing reusable and adaptable workflows for epidemiological analysis and dynamic report generation to improve disease surveillance. Important components of this work include: data access, development environment, computational resources and cloud-based management. The development environment relies on Git for code collaboration and version control and the R language for statistical computing and data visualization. The computational resources include both local and cloud-based systems, with automatic workflows managed in the cloud. The workflows are designed to be flexible and adaptable to changing data sources and stakeholder demands, with the ultimate goal to create a robust infrastructure for the delivery of actionable epidemiological information.

Corrigendum: Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.

[This corrects the article DOI: 10.3389/fvets.2021.633977.].

Gastrointestinal parasites in Swedish pigs: Prevalence and associated risk factors for infection in herds where animal welfare standards are improved.

The global pig production has undergone major changes over the past 30 years with larger farms, more intensified production as well as improved hygiene and biosecurity practices. To investigate whether these changes, along with expanded pig welfare, have had an impact on parasite occurrence, a cross-sectional study was conducted in Sweden on farms where the pigs are always loose-housed, floors are solid and bedding material is provided. A total of 1615 faecal samples were collected on 42 conventional indoor farms from a) post-weaning piglets (n = 337); b) growers (n = 345); c) fatteners (n = 308); d) dry sows (n = 277) and e) pre-partum sows (n = 348). Samples were analysed using centrifugal flotation with a saturated glucose-salt solution and a modified McMaster technique, with a lower detection limit of 50 eggs or oocysts per gram. Samples positive for strongyle-type eggs were cultured to third stage larvae for genus identification. Farms also responded to a questionnaire regarding biosecurity, hygienic measures, and other management routines. Risk factors for parasite occurrence were assessed using mixed-effects logistical regression to account for farm-level clustering of samples. Interestingly, the prevalence of Ascaris suum was reduced compared to a similar investigation in the 1980s. In the present study A. suum was detected only in 43 % of the herds, with the highest prevalence in pre-partum sows (37 %) followed by fatteners (25 %). Small sized farms were associated with higher odds of being positive, compared to large sized farms (OR = 159.1, P = 0.010). Oesophagostomum spp. were detected in 64 % of the herds and again mainly in pre-partum sows (63 %). Trichuris suis was detected in 10 % of the herds but only in <1% of the samples. Moreover, Cystoisospora suis and Eimeria spp. were detected on 60 % and 64 % the farms, with the highest prevalence in post-weaning piglets and sows, respectively. Anthelmintic drugs (ivermectin or fenbendazole) were commonly used and administered mainly to pre-partum sows on 93 % of the farms. Toltrazuril against neonatal coccidiosis was administered to piglets on 14 % of the farms. The use of antiparasitic drugs did not significantly affect parasite prevalence. Overall, it appears that the altered farming routines with focus on improved pig welfare have not solely resulted in a higher occurrence of parasites, most likely due to the adequate biosecurity and hygiene practices instituted. Thus, there seems to be no conflict between implementing measures to promote pig welfare and adequately control the more pathogenic and economically important parasites.

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.

The biggest change brought about by the "era of big data" to health in general, and epidemiology in particular, relates arguably not to the volume of data encountered, but to its variety. An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability. We review some of the opportunities for connecting data, generating information, and supporting decision-making across the increasingly complex "variety" dimension of data in population health, to enable data-driven surveillance to go beyond simple signal detection and support an expanded set of surveillance goals.

Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits.

BACKGROUND: The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. RESULTS: The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. CONCLUSIONS: The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.

Drivers for the development of an Animal Health Surveillance Ontology (AHSO).

Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

Evaluation of Strategies to Control a Potential Outbreak of Foot-and-Mouth Disease in Sweden.

To minimize the potential consequences of an introduction of foot-and-mouth disease (FMD) in Europe, European Union (EU) member states are required to present a contingency plan. This study used a simulation model to study potential outbreak scenarios in Sweden and evaluate the best control strategies. The model was informed by the Swedish livestock structure using herd information from cattle, pig, and small ruminant holdings in the country. The contact structure was based on animal movement data and studies investigating the movements between farms of veterinarians, service trucks, and other farm visitors. All scenarios of outbreak control included depopulation of detected herds, 3 km protection and 10 km surveillance zones, movement tracing, and 3 days national standstill. The effect of availability of surveillance resources, i.e., number of field veterinarians per day, and timeliness of enforcement of interventions, was assessed. With the estimated currently available resources, an FMD outbreak in Sweden is expected to be controlled (i.e., last infected herd detected) within 3 weeks of detection in any evaluated scenario. The density of farms in the area where the epidemic started would have little impact on the time to control the outbreak, but spread in high density areas would require more surveillance resources, compared to areas of lower farm density. The use of vaccination did not result in a reduction in the expected number of infected herds. Preemptive depopulation was able to reduce the number of infected herds in extreme scenarios designed to test a combination of worst-case conditions of virus introduction and spread, but at the cost of doubling the number of herds culled. This likely resulted from a combination of the small outbreaks predicted by the spread model, and the high efficacy of the basic control measures evaluated, under the conditions of the Swedish livestock industry, and considering the assumed control resources available. The results indicate that the duration and extent of FMD outbreaks could be kept limited in Sweden using the EU standard control strategy and a 3 days national standstill.

Pasture is a risk factor for Toxoplasma gondii infection in fattening pigs.

As consumer awareness of animal welfare increases throughout Europe, housing of pigs in more animal-friendly systems is becoming more common. There is concern that these free-range and organic management systems increase the prevalence of zoonotic meat-borne pathogens, such as Toxoplasma gondii. In this study we compared the seroprevalence of T. gondii between commercial fattening pigs raised on conventional and on organic farms in Sweden. Furthermore, potential associations between presence of T. gondii antibodies and type of production, access to pasture, and geographical region were analysed. A significant difference in T. gondii seroprevalence was found between conventional (1%) and organic pigs (8%). The higher odds of seropositivity in organic production was attributed to pasture access specifically (OR=1.8 for a one-month increase in length of pasture exposure). This study shows that the prevalence of T. gondii in Swedish conventional pigs is low. However, as pigs with access to pasture are at higher risk of infection and because the demand for animal-friendly production systems is increasing, there is an obvious need to practically manage the higher T. gondii presence in products from pigs raised in organic systems with outdoor access.

Vector-borne disease surveillance in livestock populations: A critical review of literature recommendations and implemented surveillance (BTV-8) in five European countries.

Preparedness against vector-borne threats depends on the existence of a long-term, sustainable surveillance of vector-borne disease and their relevant vectors. This work reviewed the availability of such surveillance systems in five European countries (Denmark, France, The Netherlands, Sweden and United Kingdom, part of the CoVetLab network). A qualitative assessment was then performed focusing on surveillance directed particularly to BTV-8. Information regarding surveillance activities were reviewed for the years 2008 and 2012. The results were then complemented with a critical scoping review of the literature aimed at identifying disease surveillance strategies and methods that are currently suggested as best suited to target vector-borne diseases in order to guide future development of surveillance in the countries in question. Passive surveillance was found to be efficient for early detection of diseases during the early phase of introduction into a free country. However, its value diminished once the disease has been established in a territory. Detection of emerging diseases was found to be very context and area specific, and thus active surveillance designs need to take the available epidemiological, ecological and entomological information into account. This was demonstrated by the effectiveness of the bulk milk surveillance in detecting the first case in Sweden, highlighting the need for output based standards to allow the most effective, context dependent, surveillance strategies to be used. Preparedness was of fundamental importance in determining the timeliness of detection and control in each country and that this in turn was heavily influenced by knowledge of emerging diseases in neighboring countries. Therefore it is crucial to share information on outbreaks between researchers and decision-makers and across borders continuously in order to react timely in case of an outbreak. Furthermore, timely reaction to an outbreak was heavily influenced by availability of control measures (vaccines), which is also strengthened if knowledge is shared quickly between countries. The assessment of the bluetongue surveillance in the affected countries showed that the degree of voluntary engagement varied, and that it is important to engage the public by general awareness and dissemination of results. The degree of engagement will also aid in establishing a passive surveillance system.

Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011-2016).

This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.