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

Machine learning (ML) is an approach to artificial intelligence characterised by the use of algorithms that improve their own performance at a given task (e.g. classification or prediction) based on data and without being explicitly and fully instructed on how to achieve this. Surveillance systems for animal and zoonotic diseases depend upon effective completion of a broad range of tasks, some of them amenable to ML algorithms. As in other fields, the use of ML in animal and veterinary public health surveillance has greatly expanded in recent years. Machine learning algorithms are being used to accomplish tasks that have become attainable only with the advent of large data sets, new methods for their analysis and increased computing capacity. Examples include the identification of an underlying structure in large volumes of data from an ongoing stream of abattoir condemnation records, the use of deep learning to identify lesions in digital images obtained during slaughtering, and the mining of free text in electronic health records from veterinary practices for the purpose of sentinel surveillance. However, ML is also being applied to tasks that previously relied on traditional statistical data analysis. Statistical models have been used extensively to infer relationships between predictors and disease to inform risk-based surveillance, and increasingly, ML algorithms are being used for prediction and forecasting of animal diseases in support of more targeted and efficient surveillance. While ML and inferential statistics can accomplish similar tasks, they have different strengths, making one or the other more or less appropriate in a given context.

Where disease risks are heterogeneous across population groups or space, or dependent on transmission between individuals, spatial data on population distributions - human, livestock and wildlife - are required to estimate infectious disease risks, burdens and dynamics. As a result, large-scale, spatially explicit, high-resolution human population data are being increasingly used in a wide range of animal- and public-health planning and policy development scenarios. Official census data, aggregated by administrative unit, provide the only complete enumeration of a country's population. While census data from developed countries are generally up-to-date and of high quality, in resource-poor settings they are often incomplete, out of date, or only available at the country or province level. The challenges associated with producing accurate population estimates in regions that lack high-quality census data have led to the development of census-independent approaches to small-area population estimations. Known as bottom-up models, as opposed to the census-based top-down approaches, these methods combine microcensus survey data with ancillary data to provide spatially disaggregated population estimates in the absence of national census data. This review highlights the need for high-resolution gridded population data, discusses problems associated with using census data as top-down model inputs, and explores census-independent, or bottom-up, methods of producing spatially explicit, high-resolution gridded population data, together with their advantages.

Monitoring antimicrobial use (AMU) and antimicrobial resistance (AMR) on farms is recognised as an important component of antimicrobial stewardship, yet the process can be resource intensive. This paper describes a subset of findings from the first year of a collaboration across government, academia and a private sector veterinary practice focused on swine production in the Midwestern United States. The work is supported by participating farmers and the greater swine industry. Twice-annual collection of samples from pigs along with AMU monitoring occurred on 138 swine farms. Detection and resistance of Escherichia coli from pig tissues was assessed, and associations between AMU and AMR were evaluated. This paper describes the methods utilised and the first-year E. coli-related results from this project. Higher minimum inhibitory concentrations (MIC) for enrofloxacin and danofloxacin in E. coli from swine tissues were associated with the purchase of fluoroquinolones. There were no other significant associations between MIC and AMU combinations in E. coli isolated from pig tissues. This project represents one of the first attempts to monitor AMU as well as AMR in E. coli in a large-scale commercial swine system in the United States of America.

Drivers are factors that have the potential to directly or indirectly influence the likelihood of infectious diseases emerging or re-emerging. It is likely that an emerging infectious disease (EID) rarely occurs as the result of only one driver; rather, a network of sub-drivers (factors that can influence a driver) are likely to provide conditions that allow a pathogen to (re-)emerge and become established. Data on sub-drivers have therefore been used by modellers to identify hotspots where EIDs may next occur, or to estimate which sub-drivers have the greatest influence on the likelihood of their occurrence. To minimise error and bias when modelling how sub-drivers interact, and thus aid in predicting the likelihood of infectious disease emergence, researchers need good-quality data to describe these sub-drivers. This study assesses the quality of the available data on sub-drivers of West Nile virus against various criteria as a case study. The data were found to be of varying quality with regard to fulfilling the criteria. The characteristic with the lowest score was completeness, i.e. where sufficient data are available to fulfil all the requirements for the model. This is an important characteristic as an incomplete data set could lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good-quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and identifying the points on the risk pathway where preventive measures may be taken.

In the past ten years, with the development of computer and internet technology, the informatisation of animal health data management has continuously improved, thus strengthening the role of animal health information in supporting decision-making. This article outlines the legal basis, management system and collection procedure for animal health data in the mainland of China. Its development and application are also briefed, and its future development is envisioned based on the current situation.

Informal science education institutions such as zoos, natural history museums and botanical gardens exhibit live native and exotic insects and other arthropods to improve the general public's knowledge about these organisms and promote their conservation in nature. The purpose of this paper is to summarise the process of shipping exotic arthropods for exhibits and the regulations that apply, and to discuss issues that affect international shipment for this type of activity. These issues include escapes affecting the environment and delays affecting the viability of shipped insects. The regulatory agencies that issue permits for the importation of live insects for education and exhibit are discussed. The number of butterflies flying in the exhibits ranges from 100 specimens at the Butterfly Encounter of the Connecticut Science Center to a high of 15,000 specimens at the Dubai Butterfly Garden, with a mean of 2,048 specimens (n = 32). If the outlier of 15,000 is removed, the mean is 1,630 (n = 31). Insect zoos and butterfly exhibits play an overwhelmingly positive educational role by introducing millions of children and adults to the immensely important world of insects.

International trade in live insects involves the shipping of many different species, for various purposes, with a variety of handling requirements regulated by numerous authorities with varying objectives. The diversity of factors at play has both created and been subject to a complex regulatory landscape. A review of global production, shipping and use experiences from a range of perspectives has shown gaps and inconsistencies in international guidance and national implementation. Private carriers add another layer of uncertainty that is disproportionate to risks, resulting in variable practices and charges. Many benefits can come from international trade in insects, including pollinator services, control of pests and of disease vectors, and enhanced international scientific research and innovation. These benefits will be better achieved through a more evidence-based and efficient approach to regulating trade. This change in approach will in turn require an improved and widely accepted risk-management landscape for insect trade.

Drosophila melanogaster has been a model organism for experimental research for more than a century, and the knowledge and associated genetic technologies accumulated around this species make it extremely important to contemporary biomedical research. A large international community of highly collaborative scientists investigate a remarkable diversity of biological problems using genetically characterised strains of Drosophila, and frequently exchange these strains across borders. Despite its importance to the study of fundamental biological processes and human disease-related cellular mechanisms, and the fact that it presents minimal health, agricultural or environmental risks, Drosophila can be difficult to import. The authors argue that streamlined regulations and practices would benefit biomedical research by lowering costs and increasing efficiencies.

Insects play a crucial role in research. Many laboratories are developing technologies to control insect vectors or agricultural pests by using genetic modifications that either reduce insect reproduction or increase refractoriness to disease transmission. Those tools include gene-drive elements that may spread such genetic traits in a selfsustaining and cost-effective manner. Since international research collaborations are nowadays routine, movement of genetically modified insects between laboratories under different regulatory jurisdictions is very common. This article describes the requirements and guidelines for transportation of genetically modified insects for research. The author draws upon the experience gained by an Italian laboratory, in its role as a research centre involved in shipments of wild and modified mosquitoes, within an international research consortium developing sustainable tools for malaria control.