{"id":{"repo_id":"unsw","oai_identifier":"oai:unsworks.library.unsw.edu.au:1959.4/102855"},"canonical_url":"https://search.dev.ndltd.org/etd/unsw/oai:unsworks.library.unsw.edu.au:1959.4/102855","repository":{"repo_id":"unsw","name":"University of New South Wales","base_url":"https://unsworks.unsw.edu.au/oai/provider"},"display":{"title":"A universal outbreak risk prediction tool","abstract":"The pandemic outbreak situation has become more serious in the 21st century. Diseases have become more likely to spread across borders and pose a greater threat than before. In order to handle this new situation, the ability to predict the risk of pandemic outbreaks is a necessary. Existing prediction tools include machine learning-based tools and mathematical tools, which do not use machine learning. They both have disadvantages and advantages. My research goal is to develop an ideal outbreak prediction tool that has all the advantages at the same time and overcomes their disadvantages. To achieve this, I used an existing tool, EPIRISK, to implement three machine learning iterations and developed an automated outbreak risk prediction tool. My tool has strong data processing capabilities, it can use variables from multiple aspects to make universal risk predictions for multiple diseases and countries at around 80% to 90% accuracy. At the same time, it combines Automated machine learning and machine learning, which makes it flexible and easy to use and understand. It can allow non-machine learning experts to utilize their expert knowledge and conduct custom model building and improvement on a visual page. My predictive tool fills the research gap in current studies, it can help governments take rapid control measures in the early stages of disease outbreaks, as well as assist international cooperation.","abstract_html":"The pandemic outbreak situation has become more serious in the 21st century. Diseases have become more likely to spread across borders and pose a greater threat than before. In order to handle this new situation, the ability to predict the risk of pandemic outbreaks is a necessary. Existing prediction tools include machine learning-based tools and mathematical tools, which do not use machine learning. They both have disadvantages and advantages. My research goal is to develop an ideal outbreak prediction tool that has all the advantages at the same time and overcomes their disadvantages. To achieve this, I used an existing tool, EPIRISK, to implement three machine learning iterations and developed an automated outbreak risk prediction tool. My tool has strong data processing capabilities, it can use variables from multiple aspects to make universal risk predictions for multiple diseases and countries at around 80% to 90% accuracy. At the same time, it combines Automated machine learning and machine learning, which makes it flexible and easy to use and understand. It can allow non-machine learning experts to utilize their expert knowledge and conduct custom model building and improvement on a visual page. My predictive tool fills the research gap in current studies, it can help governments take rapid control measures in the early stages of disease outbreaks, as well as assist international cooperation.","abstract_has_math":false,"creators":["Zhang, Tianyu ; https://orcid.org/0000-0001-6332-3921"],"institution":"UNSW, Sydney","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T05:33:06Z","subjects":["Machine Learning","Public Health","Computer Science","Software Engineering","Outbreak Risk Prediction","anzsrc-for: 4611 Machine learning","anzsrc-for: 4612 Software engineering"],"languages":[],"rights":["open access","CC BY 4.0","free_to_read"],"rights_urls":["https://purl.org/coar/access_right/c_abf2","https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.26190/unsworks/30434"],"render_values":[{"text":"https://doi.org/10.26190/unsworks/30434","href":"https://doi.org/10.26190/unsworks/30434","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/1959.4/102855","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Zhang, Tianyu ; https://orcid.org/0000-0001-6332-3921"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["UNSW, Sydney"]},{"key":"dc:type","label":"Dc Type","values":["master thesis","http://purl.org/coar/resource_type/c_bdcc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Public Health","Computer Science","Software Engineering","Outbreak Risk Prediction","anzsrc-for: 4611 Machine learning","anzsrc-for: 4612 Software engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["open access","https://purl.org/coar/access_right/c_abf2","CC BY 4.0","https://creativecommons.org/licenses/by/4.0/","free_to_read"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/1959.4/102855","https://unsworks.unsw.edu.au/bitstreams/00222a1f-aeac-4bcd-89e4-7c2b3965c368/download","https://doi.org/10.26190/unsworks/30434"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The pandemic outbreak situation has become more serious in the 21st century. Diseases have become more likely to spread across borders and pose a greater threat than before. In order to handle this new situation, the ability to predict the risk of pandemic outbreaks is a necessary. Existing prediction tools include machine learning-based tools and mathematical tools, which do not use machine learning. They both have disadvantages and advantages. My research goal is to develop an ideal outbreak prediction tool that has all the advantages at the same time and overcomes their disadvantages. To achieve this, I used an existing tool, EPIRISK, to implement three machine learning iterations and developed an automated outbreak risk prediction tool. My tool has strong data processing capabilities, it can use variables from multiple aspects to make universal risk predictions for multiple diseases and countries at around 80% to 90% accuracy. At the same time, it combines Automated machine learning and machine learning, which makes it flexible and easy to use and understand. It can allow non-machine learning experts to utilize their expert knowledge and conduct custom model building and improvement on a visual page. My predictive tool fills the research gap in current studies, it can help governments take rapid control measures in the early stages of disease outbreaks, as well as assist international cooperation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A universal outbreak risk prediction tool"]}]}],"canonical_facts":{"dc:creator":["Zhang, Tianyu ; https://orcid.org/0000-0001-6332-3921"],"dc:date":["2024"],"dc:description":["The pandemic outbreak situation has become more serious in the 21st century. Diseases have become more likely to spread across borders and pose a greater threat than before. In order to handle this new situation, the ability to predict the risk of pandemic outbreaks is a necessary. Existing prediction tools include machine learning-based tools and mathematical tools, which do not use machine learning. They both have disadvantages and advantages. My research goal is to develop an ideal outbreak prediction tool that has all the advantages at the same time and overcomes their disadvantages. To achieve this, I used an existing tool, EPIRISK, to implement three machine learning iterations and developed an automated outbreak risk prediction tool. My tool has strong data processing capabilities, it can use variables from multiple aspects to make universal risk predictions for multiple diseases and countries at around 80% to 90% accuracy. At the same time, it combines Automated machine learning and machine learning, which makes it flexible and easy to use and understand. It can allow non-machine learning experts to utilize their expert knowledge and conduct custom model building and improvement on a visual page. 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