Revue Scientifique et Technique-Office International Des Epizooties最新文献

The World Animal Health Information System (WAHIS) collects and publishes a wealth of information gathered by individual countries' Veterinary Services, including detailed country-specific information on outbreaks of diseases listed by the World Organisation for Animal Health (WOAH, founded as OIE), including emerging diseases, in domestic animals and wildlife, and non-listed diseases in wildlife. The data set is one of the most comprehensive in the world, with 182 Members obliged to report this information to WOAH in a timely manner. As such, the data provide invaluable input for Veterinary Services, animal health researchers and stakeholders to gain insight into risk from infectious diseases, for example through the development of predictive models and risk assessments to address the risk from trade of animal products, globalisation, or movement of wildlife or vectors across country borders. This paper reviews previous analyses that have been conducted using WAHIS data and outlines ways in which these data can be used for preparedness and risk assessment.

In 2015, the World Organisation for Animal Health (WOAH, founded as OIE) initiated the annual collection of data on antimicrobials intended for use in animals using a Microsoft Excel questionnaire. In 2022, WOAH initiated the migration to a customised interactive online system: ANIMUSE Global Database. This system enables national Veterinary Services not only to monitor and report data more easily and more accurately, but also to visualise, analyse and use data for surveillance purposes to their own benefit in the implementation of national action plans on antimicrobial resistance. This journey started seven years ago, with progressive improvements in the way data are collected, analysed and reported and continuous adaptations to overcome various challenges encountered (e.g. data confidentiality, training of civil servants, calculation of active ingredients, standardisation to enable fair comparisons and trend analyses, and data interoperability). Technical developments have been key in the success of this endeavour. However, it is important not to underestimate the importance of the human element: to listen to WOAH Members and their needs, and to exchange to solve issues, adapt tools, and gain and maintain trust. The journey is not over yet, and more developments are foreseen, such as to complement current data sources with data collected directly at the farm level; strengthen interoperability and integrated analysis with cross-sectoral databases; and facilitate institutionalisation of data collection and systematic use in monitoring, evaluation, lesson learning, reporting and, eventually, surveillance of antimicrobial use and antimicrobial resistance when implementing and updating national action plans. This paper describes how all these challenges were overcome and how future challenges will be addressed.

Animal health surveillance, despite its name, tends to focus on looking for disease. Often this involves searching for cases of infection with known pathogens (‘pathogen chasing'). Such an approach is both resource intensive and limited by the requirement for prior knowledge of disease likelihood. In this paper, the authors propose the gradual reshaping of surveillance towards the systems level, focusing on the processes (‘drivers') that promote disease or health, rather than on the presence or absence of specific pathogens. Examples of relevant drivers include land-use change, increasing global interconnectedness, and finance and capital flows. Importantly, the authors suggest that surveillance should focus on detecting changes in patterns or quantities associated with such drivers. This would generate systems-level, risk-based surveillance information to identify areas where additional attention may be needed, and, over time, inform the implementation of prevention efforts. The collection, integration and analysis of data on drivers is likely to require investment in improving data infrastructures. A period of overlap would allow the two systems (traditional surveillance and driver monitoring) to be compared and calibrated. This would also lead to a better understanding of the drivers and their linkages, and thereby generate new knowledge that can improve surveillance and inform mitigation efforts. Since surveillance of drivers may give signals when changes are occurring, which could act as alerts and enable targeted mitigation, this might even enable disease to be prevented before it happens by directly intervening in the drivers themselves. Such surveillance focused on the drivers could be expected to bring additional benefits, since the same drivers promote multiple diseases. Further, focusing on drivers rather than pathogens should enable control of currently unknown diseases, making this approach particularly timely, given the increasing risk of emergence of new diseases.

African swine fever (ASF) and classical swine fever (CSF) are transboundary animal diseases (TADs) of pigs. Much effort and resources are regularly put into preventing these diseases' introduction in free areas. Passive surveillance activities bring the highest chances for the early detection of TAD incursions because they are routinely and widely conducted at farms, and because these activities focus on the time between introduction and when the first sample is sent for diagnostic testing. The authors proposed the implementation of an enhanced passive surveillance (EPS) protocol based on collecting data through participatory surveillance actions using an objective and adaptable scoring system to aid the early detection of ASF or CSF at the farm level. The protocol was applied in two commercial pig farms for ten weeks in the Dominican Republic, which is a CSF- and ASF-infected country. This study was a proof of concept, based on the EPS protocol to aid detection of substantial variations in the risk score triggering testing. One of the followed farms had score variation, which triggered testing of the animals, although the test results were negative. The study enables assessment of some of the weaknesses associated with passive surveillance and provides lessons applicable to the problem. Results demonstrate the potential for overcoming some issues preventing the broad application of EPS protocols and suggest that standardised approaches may contribute to the early detection of CSF and ASF introductions.

The Global Burden of Animal Diseases (GBADs) programme will provide data-driven evidence that policy-makers can use to evaluate options, inform decisions, and measure the success of animal health and welfare interventions. The GBADs' Informatics team is developing a transparent process for identifying, analysing, visualising and sharing data to calculate livestock disease burdens and drive models and dashboards. These data can be combined with data on other global burdens (human health, crop loss, foodborne diseases) to provide a comprehensive range of information on One Health, required to address such issues as antimicrobial resistance and climate change. The programme began by gathering open data from international organisations (which are undergoing their own digital transformations). Efforts to achieve an accurate estimate of livestock numbers revealed problems in finding, accessing and reconciling data from different sources over time. Ontologies and graph databases are being developed to bridge data silos and improve the findability and interoperability of data. Dashboards, data stories, a documentation website and a Data Governance Handbook explain GBADs data, now available through an application programming interface. Sharing data quality assessments builds trust in such data, encouraging their application to livestock and One Health issues. Animal welfare data present a particular challenge, as much of this information is held privately and discussions continue regarding which data are the most relevant. Accurate livestock numbers are an essential input for calculating biomass, which subsequently feeds into calculations of antimicrobial use and climate change. The GBADs data are also essential to at least eight of the United Nations Sustainable Development Goals.

Risk assessment is an essential tool used in the control of disease outbreaks. Without it, key risk pathways might not be identified, resulting in potential spread of disease. The devastating effects of disease spread can ripple through society, affecting the economy and trade and having considerable impact on animal health and potentially human health. The World Organisation for Animal Health (WOAH, founded as OIE) has highlighted that risk analysis, which includes risk assessment, is not consistently used across all Members, with some low-income countries making policy decisions without prior risk assessment. The failure of some Members to rely on risk assessment could be caused by a lack of staff and risk assessment-related training, poor funding in the animal health sector, and lack of understanding regarding the use and application of risk analysis. However, to complete effective risk assessment, high-quality data must be collected, and other factors such as geographical conditions, use (or not) of technology, and varying production systems all influence the ability to collect these data. Demographic and population-level data can be collected during peacetime in the form of surveillance schemes and national reports. Having these data before an outbreak occurs better equips a country for controlling or preventing disease outbreaks. In order for all WOAH Members to meet risk analysis requirements, an international effort must be made for cross-working and the development of collaborative schemes. Technology can play an important role in the development of risk analysis, and low-income countries must not be left behind in the efforts to protect animal and human populations from disease.

Those who work in the area of surveillance and prevention of emerging infectious diseases (EIDs) face a challenge in accurately predicting where infection will occur and who (or what) it will affect. Establishing surveillance and control programmes for EIDs requires substantial and long-term commitment of resources that are limited in nature. This contrasts with the unquantifiable number of possible zoonotic and non-zoonotic infectious diseases that may emerge, even when the focus is restricted to diseases involving livestock. Such diseases may emerge from many combinations of, and changes in, host species, production systems, environments/habitats and pathogen types. Given these multiple elements, risk prioritisation frameworks should be used more widely to support decision-making and resource allocation for surveillance. In this paper, the authors use recent examples of EID events in livestock to review surveillance approaches for the early detection of EIDs, and highlight the need for surveillance programmes to be informed and prioritised by regularly updated risk assessment frameworks. They conclude by discussing some unmet needs in risk assessment practices for EIDs, and the need for improved coordination in global infectious disease surveillance.

Disease emergence represents a global threat to public health, economy and biological conservation. Most emerging zoonotic diseases have an animal origin, most commonly from wildlife. To prevent their spread and to support the implementation of control measures, disease surveillance and reporting systems are needed, and due to globalisation, these activities should be carried out at the global level. To define the main gaps affecting the performance of wildlife health surveillance and reporting systems globally, the authors analysed data from a questionnaire sent to National Focal Points of the World Organisation for Animal Health that inquired on structure and limits of wildlife surveillance and reporting systems in their territories. Responses from 103 Members, covering all areas of the globe, revealed that 54.4% have a wildlife disease surveillance programme and 66% have implemented a strategy to manage disease spread. The lack of dedicated budget affected the possibility of outbreak investigations, sample collection and diagnostic testing. Although most Members maintain records relating to wildlife mortality or morbidity events in centralised databases, data analysis and disease risk assessment are reported as priority needs. The authors' evaluation of surveillance capacity found an overall low level, with marked variability among Members that was not restricted to a specific geographical area. Increased wildlife disease surveillance globally would help in understanding and managing risks to animal and public health. Moreover, consideration of the influence of socio-economic, cultural and biodiversity aspects could improve disease surveillance under a One Health approach.

Advances in technology and decreasing costs have accelerated the use of high-throughput sequencing (HTS) for both diagnosis and characterisation of infectious animal diseases. High-throughput sequencing offers several advantages over previous techniques, including rapid turnaround times and the ability to resolve single nucleotide changes among samples, both of which are important for epidemiological investigations of outbreaks. However, due to the plethora of genetic data being routinely generated, the storage and analysis of these data are proving challenging in their own right. In this article, the authors provide insight into the aspects of data management and analysis that should be considered before adopting HTS for routine animal health diagnostics. These elements fall largely into three interrelated categories: data storage, data analysis and quality assurance. Each has numerous complexities and may need to be adapted as HTS evolves. Making appropriate strategic decisions about bioinformatic sequence analysis early on in project development will help to avert major issues in the long term.

Evidence-based decision-making is now axiomatic in many sectors and has become increasingly important in prioritising development in low- and middle-income countries. In the livestock development sector, there has been a lack of data on health and production required to establish an evidence base. Thus, much strategic and policy decision-making has been based on the more subjective grounds of opinion, expert or otherwise. However, there is now a trend towards a more data-driven approach for such decisions. The Centre for Supporting Evidence-Based Interventions in Livestock was established in Edinburgh by the Bill and Melinda Gates Foundation in 2016, to collate and publish livestock health and production data, lead a community of practice to harmonise livestock-data-related methodologies, and develop and monitor performance indicators for livestock investments.