[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119477-en":3,"doc-seo-119477-105":30,"detail-sidebar-cat-0-en-105":91},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},119477,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Advanced Analytics for Disease Forecasting - A Comparative Analysis of Statistical and Machine Learning Methods - Thesis Abstract","Infectious diseases continue to evolve and require surveillance systems that can anticipate changing patterns. This thesis evaluates statistical and machine learning approaches for disease forecasting and outbreak detection using temporal and spatio-temporal infectious disease surveillance data from Canada. Focused applications include seasonal influenza, COVID-19, and Lyme disease, with comparisons across model families. Seasonal ARIMA outperforms neural networks for influenza, while random forests improve COVID-19 forecasting. Machine learning also forecasts spatio-temporal Lyme incidence, whereas Bayesian statistics did not converge.","Advanced Analytics for Disease Forecasting – A Comparative Analysis of Statistical and Machine Learning Methods  \nby  \nArmin Orang  \nA Thesis  \npresented to  \nThe University of Guelph  \nIn partial fulfilment of requirements  \nfor the degree of  \nDoctor of Philosophy  \nin  \nPopulation Medicine  \nin the field of Epidemiology  \nGuelph, Ontario, Canada  \n© Armin Orang, January, 2024  \nABSTRACT  \nADVANCED ANALYTICS FOR DISEASE FORECASTING – A COMPARATIVE  \nANALYSIS OF STATISTICAL AND MACHINE LEARNING  \nArmin Orang  \nUniversity of Guelph, 2024  \nAdvisor(s):  \nDr. Olaf Berke  \nDr. Victoria Ng  \nInfectious diseases continue to evolve and as seen during the recent coronavirus (COVID-19) pandemic, they remain a serious threat to the health of human populations. To keep pace with the evolution of infectious diseases, surveillance methods must similarly advance. This thesis aims to explore the application of statistical and machine learning methods for disease forecasting and outbreak detection. Methods were applied to temporal and spatio-temporal data from infectious disease surveillance in Canada.  \nInfectious diseases of interest were seasonal influenza, COVID-19, and Lyme disease. Accurately forecasting the timing and magnitude of peak seasonal influenza incidence is important for public health preparedness. In 2019, COVID-19 emerged and posed a substantial risk to the health of Canadians. COVID-19 incidence data provide the opportunity to evaluate the statistical and machine learning ability to model emerging diseases. The expanding geographic range of Lyme disease is also a growing concern to Canadian public health. As Lyme disease incidence has been linked to changing weather patterns, projecting its incidence under the different climate scenarios of Representative Concentration Pathway 4.5 and 8.5 is of interest.  \nSeasonal Autoregressive Integrated Moving Average was shown to outperform artificial neural networks in forecasting seasonal influenza activity in Canada. However, when applied to COVID-19 incidence in the public health units of Toronto and Wellington-Dufferin-Guelph, random forest outperformed several statistical learning models. Additionally, machine learning accurately forecasted spatio-temporal Lyme disease incidence in Ontario. For the same dataset, Bayesian statistics did not converge.  \nEndemic-Epidemic modeling showed solid performance in measures of power of detection, sensitivity, specificity, and timeliness for detecting simulated COVID-19 outbreaks inspatio-temporal data structures. Farrington Flexible (FF) required tuning before demonstrating robust performance.  \nResults indicate that both statistical and machine learning are valuable for disease surveillance. Machine learning is a flexible tool, displaying strong forecasting performance across different data structures. With advances in computational power and availability of “big data”, machine learning will continue to play an important role in disease forecasting. However, the “black box” problem of machine learning makes it unfit for explanatory purposes. Therefore, traditional statistical models should still be applied to identify possible risk factors for disease incidence.  \nDEDICATION  \nThis work is dedicated to my loved ones. My partner Samantha, who kept me grounded throughout this journey. If not for your dedication, love and endless support, this thesis would not have been possible. And to my strange one Drew, whose brought boundless joy to my life.  \nACKNOWLEDGEMENTS  \n“Ifyou want to build a ship, don’t drum up people to collect wood and don’t assign them tasks and work, but rather teach them to long for the endless immensity of the sea.”  \n– Antoine de Saint-Exupéry  \nI am grateful to the people who embarked on this adventure with me-my mentors, who steered me through the immensity of the sea, and my family, who have been my anchor along the way.  \nI would like to express my deepest gratitude to Dr. Olaf Berke for taking me under his supervi","cbCail89uujTuSSf","https://ap.wps.com/l/cbCail89uujTuSSf","pdf",2788380,1,210,"English","en",105,"# Abstract\n# Disease Surveillance and Modeling Goals\n## Target Diseases and Data Sources\n## Statistical and Machine Learning Methods\n# Comparative Results and Implications\n## Forecasting Performance Across Models\n## Outbreak Detection and Model Sensitivity","[{\"question\":\"What diseases and data structures are analyzed in this thesis?\",\"answer\":\"The thesis applies models to seasonal influenza, COVID-19, and Lyme disease using temporal and spatio-temporal surveillance data from Canada, including geographic and time-varying structures.\"},{\"question\":\"Which statistical and machine learning methods performed best for seasonal influenza and COVID-19?\",\"answer\":\"Seasonal ARIMA outperformed artificial neural networks for forecasting seasonal influenza activity. For COVID-19, random forest outperformed several statistical learning models in the Toronto and Wellington-Dufferin-Guelph public health units.\"},{\"question\":\"How effective was the work for outbreak detection and what limitation was observed for Bayesian methods?\",\"answer\":\"Endemic-Epidemic modeling showed strong performance using power of detection, sensitivity, specificity, and timeliness on simulated COVID-19 outbreaks. Farrington Flexible required tuning for robust performance, and Bayesian statistics did not converge on the referenced dataset.\"}]","Advanced Analytics for Disease Forecasting - A Comparative Analysis of Statistical and Machine Learning Methods - Thesis Abstract | PDF",1785724516,529,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advanced-analytics-for-disease-forecasting-a-comparative-analysis-of-statistical-and-machine-learning-methods-thesis-abstract","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advanced-analytics-for-disease-forecasting-a-comparative-analysis-of-statistical-and-machine-learning-methods-thesis-abstract/119477/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What diseases and data structures are analyzed in this thesis?","Question",{"text":75,"@type":76},"The thesis applies models to seasonal influenza, COVID-19, and Lyme disease using temporal and spatio-temporal surveillance data from Canada, including geographic and time-varying structures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which statistical and machine learning methods performed best for seasonal influenza and COVID-19?",{"text":80,"@type":76},"Seasonal ARIMA outperformed artificial neural networks for forecasting seasonal influenza activity. For COVID-19, random forest outperformed several statistical learning models in the Toronto and Wellington-Dufferin-Guelph public health units.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective was the work for outbreak detection and what limitation was observed for Bayesian methods?",{"text":84,"@type":76},"Endemic-Epidemic modeling showed strong performance using power of detection, sensitivity, specificity, and timeliness on simulated COVID-19 outbreaks. Farrington Flexible required tuning for robust performance, and Bayesian statistics did not converge on the referenced dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]