Data distribution in public veterinary service: health and safety challenges push for context-aware systems

Abstract

BackgroundToday’s globalised and interconnected world is characterized by intertwined and quickly evolving relationships between animals, humans and their environment and by an escalating number of accessible data for public health. The public veterinary services must exploit new modeling and decision strategies to face these changes. The organization and control of data flows have become crucial to effectively evaluate the evolution and safety concerns of a given situation in the territory. This paper discusses what is needed to develop modern strategies to optimize data distribution to the stakeholders.Main textIf traditionally the system manager and knowledge engineer have been concerned with the increase of speed of data flow and the improvement of data quality, nowadays they need to worry about data overflow as well. To avoid this risk an information system should be capable of selecting the data which need to be shown to the human operator. In this perspective, two aspects need to be distinguished: data classification vs data distribution. Data classification is the problem of organizing data depending on what they refer to and on the way they are obtained; data distribution is the problem of selecting which data is accessible to which stakeholder. Data classification can be established and implemented via ontological analysis and formal logic but we claim that a context-based selection of data should be integrated in the data distribution application. Data distribution should provide these new features: (a) the organization of situation types distinguishing at least ordinary vs extraordinary scenarios (contextualization of scenarios); (b) the possibility to focus on the data that are really important in a given scenario (data contextualization by scenarios); and (c) the classification of which data is relevant to which stakeholder (data contextualization by users).Short conclusionPublic veterinary services, to efficaciously and efficiently manage the information needed for today’s health and safety challenges, should contextualize and filter the continuous and growing flow of data by setting suitable frameworks to classify data, users’ roles and possible situations.

Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea

“…In an emergency brought about by an outbreak of a transboundary animal disease, such as HPAI, readily available information is necessary for both the national and local animal health authorities to deal with time pressures, insufficient human and physical resources, and general uncertainty and to implement the appropriate response [ 28 ]. Decision-making support systems had been implemented decades ago for emergency disease control against diseases in swine, such as foot-and-mouth disease or classical swine fever, and measures have been established to comprehensively utilize information-based on the advice of experts and through simulation modeling [ 29 – 31 ].…”

Systematic Literature Review of Models Used in the Epidemiological Analysis of Bovine Infectious Diseases

“…Solving these problems will require a multidisciplinary approach and close collaboration between computer scientists, statisticians, public health experts, and policy analysts [23,27]. Such an approach would make use of mathematical models and computer tools to effectively and efficiently manage the information needed today [5,26], and thus propose solutions to these problems [61].…”

“…Furthermore, the regular feeding of populations with noble proteins derived from milk, egg, or meat is vital, and their lack constitutes a public health problem [3]. Experts in [3,5] indicate that 20% of global production is lost due to diseases that affect animals, suggesting that even animal diseases that are non-contagious to humans could generate serious public health problems due to the deprivation that they can cause [5]. These topics are addressed from different fields of science such as medicine, veterinary medicine, economics, informatics, statistics, and mathematics, generating new fields of research and development that seek to mitigate losses and join efforts to improve human health welfare.…”

Digital technologies and implications for Veterinary Services

“…Data will become central to veterinary systems, whether specific and small-scale data customised for routine disease reporting or big data from various sources for the identification of risk factors and trends in disease patterns. It is therefore essential that national Veterinary Services strengthen their capabilities and infrastructures for data use and accessibility through interoperability, harmonisation and optimisation of data distribution to stakeholders (70). It is equally important to develop collaborative models and tools for information sharing beyond the animal health sector, through the exchange of data across sectors and value chains for various purposes (71), and by ensuring interoperability of data systems which must be able to 'talk' to each other (34).…”