[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117276-en":3,"doc-seo-117276-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117276,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning for Livestock Disease Surveillance - PhD Thesis","Machine learning methods are expected to strengthen the United Kingdom economy and expand growth in animal health and surveillance. This PhD project evaluates how ML can support decision-making in livestock disease surveillance and improve prediction of future disease breakdowns, using bovine tuberculosis as a key exemplar due to its global and UK economic burden and available surveillance data. Models predict herd-level and individual-animal breakdowns, correlate test results with risk factors, and show improved diagnostic sensitivity and future predictive performance, supporting more effective disease control.","This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e. g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use:  \n• This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated.  \n• A copy can be downloaded for personal non-commercial research or study, without prior permission or charge.  \n• This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author.  \n• The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author.  \n• When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.  \nMachine Learning for Livestock Disease Surveillance  \nKajetan Stanski  \nName of degree: Genetics and Genomics (PhD) (Full-time) The University of Edinburgh  \nYear of presentation: 5  \nDeclaration  \nI, Kajetan Stanski declare that:  \na) the thesis has been composed by me (the student)  \nb) the work is my own  \nc) the work has not been submitted for any other degree or professional qualification  \nd) Contributions to the publication in Chapter 2 are as follows: Mark Bronsvoort developed the original project plan , I, Kajetan Stanski collated the data, trained models and wrote the manuscript , All authors (including Samantha Lycett and Thibaud Porphyre) contributed to the discussion, interpretation of results and reviewed the manuscript.  \nEdinburgh, 2021/10/29  \nAbstract  \nMachine learning (ML) methods are forecasted to increase United Kingdom (UK)’s economy by over £200bn by 2030 and one of the sectors which will see the most growth is animal health and surveillance. This PhD project aims to explore possibilities to apply ML to better support decision making in livestock disease surveillance and assess ML models as a way to accurately predict future disease breakdowns. Bovine tuberculosis (bTB) is an exemplar livestock disease in this project and it was chosen because of its negative economic impact in UK and globally and due to availability of bTB surveillance data. Despite decades of control efforts, bTB has not been controlled in UK and currently costs ~£100m annually. Critical in the failure of control efforts has been the lack of a sufficiently sensitive diagnostic test. In the current bTB control programme, the test results inform disease control measures both at herd-level (between-farm animal movement restrictions) and at animal-level (follow up testing and culling of infected animals) . Increasing the test sensitivity can therefore improve the efficacy of mitigation activities and reduce impact of bTB on the cattle industry.  \nIn this project, ML was used to predict herd-level bTB breakdowns in Great Britain (GB) with the aim of improving herd-level diagnostic sensitivity. The results of routinely-collected herd-level tests were correlated with risk factor data: bTB breakdown history, between-farm cattle movements and geographical locations of the farms. Four ML methods were independently trained with data from 2012–2014 including ~4,700 positive herd-level test results annually. The best model’s performance was compared to the observed sensitivity and specificity of the herdlevel test calculated on the 2015 data. The best ML algorithm showed a high predictivity of bTB infection, with an area under receiver operating characteristic curve (AUC) of 0.907. Once compared with the currently used interpretation of the test, the ML algorithm increased herd-level sensitivity from 61.3% to 67.6% and herdlevel specificity from 90.5% to 92.3% . This approach can improve predictive capability for herd-level bTB and support disease control.  \nIndividual animal-level bTB breakdown predictions are complimentary to the herdlevel ones as they can inform animal-level dis","cbCaiktYBEFXywrw","https://ap.wps.com/l/cbCaiktYBEFXywrw","pdf",2541228,1,161,"English","en",105,"# Declaration\n## Contributions\n# Abstract\n## Herd-level bTB prediction in Great Britain\n## Individual animal-level bTB prediction in Republic of Ireland\n## Movement network integration for improved prediction\n## Model evaluation and performance metrics","[{\"question\":\"What is the main objective of the PhD project?\",\"answer\":\"To explore how machine learning can support decision-making in livestock disease surveillance and accurately predict future disease breakdowns, with bovine tuberculosis as the exemplar disease.\"},{\"question\":\"How were herd-level bTB breakdowns predicted?\",\"answer\":\"Routinely collected herd-level test results were correlated with risk factor data such as breakdown history, between-farm cattle movements, and farm geography, using independently trained ML methods on 2012–2014 data.\"},{\"question\":\"How did individual animal-level predictions differ from herd-level predictions?\",\"answer\":\"Individual-level models used additional risk factors (age, sex, and breed) and provided probabilities of infection after a negative diagnostic test, improving AUC for predictions made 183 and 365 days into the 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is the main objective of the PhD project?","Question",{"text":74,"@type":75},"To explore how machine learning can support decision-making in livestock disease surveillance and accurately predict future disease breakdowns, with bovine tuberculosis as the exemplar disease.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were herd-level bTB breakdowns predicted?",{"text":79,"@type":75},"Routinely collected herd-level test results were correlated with risk factor data such as breakdown history, between-farm cattle movements, and farm geography, using independently trained ML methods on 2012–2014 data.",{"name":81,"@type":72,"acceptedAnswer":82},"How did individual animal-level predictions differ from herd-level predictions?",{"text":83,"@type":75},"Individual-level models used additional risk factors (age, sex, and breed) and provided probabilities of infection after a negative diagnostic test, improving AUC for predictions made 183 and 365 days into the 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