{"689786":{"#nid":"689786","#data":{"type":"event","title":"CHHS Webinar Series: \u0022Infectious Disease Forecasting with Digital Data Streams: Comparing AI Transformers with Classical Statistical Methods\u0022","body":[{"value":"\u003Cp\u003EAccurate and timely forecasting of infectious diseases is critical for public health decision-making. Over the past decade, digital data streams such as internet search queries, electronic health records, and pharmacy sales data have emerged as powerful supplements to traditional surveillance systems.\u003C\/p\u003E\u003Cp\u003EThis seminar presents a synthesis of research focused on leveraging these data sources for infectious disease prediction, spanning from classical statistical approaches to modern AI architectures.\u003C\/p\u003E\u003Ch3\u003EKey Areas of Discussion:\u003C\/h3\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStatistical Methodologies:\u003C\/strong\u003E The presentation covers a family of models that combine autoregressive time series structure with penalized regression on Google search data. This includes extensions to spatial-temporal modeling, multi-disease settings, and COVID-19 adaptation.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI Architectures:\u003C\/strong\u003E Recent work on attention-based transformer architectures for time series forecasting will be discussed, highlighting methods for multivariate dependency modeling, in-context learning, and efficient linear attention.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EPractical Application:\u003C\/strong\u003E Drawing from ongoing participation in the CDC FluSight forecasting initiative, the session compares the strengths and limitations of both paradigms and shares practical lessons learned from real-time deployment.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EThe talk aims to offer perspective on when and how statistical rigor and deep learning flexibility each contribute to reliable disease forecasting.\u003C\/p\u003E\u003Ch3\u003EAbout the Speaker\u003C\/h3\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/shihao-yang\u0022\u003E\u003Cstrong\u003EDr. Shihao Yang\u003C\/strong\u003E\u003C\/a\u003E is the Harold E. Smalley Early Career Professor and Assistant Professor in the School of Industrial and Systems Engineering at Georgia Tech. He completed his PhD in statistics and post-doc in Biomedical Informatics at Harvard University. Dr. Yang\u2019s research focuses on data science, with special interest in time series, dynamical systems, and applications in infectious disease transmission forecasting.\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/webinar\/register\/WN_rGkOsJm9QLq2T4KXVZ1Mcw#\/registration\u0022\u003ETo attend, please register online via Zoom\u003C\/a\u003E.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EMove beyond slow, traditional reporting and explore how modern AI uses everyday data, like internet queries and sales records, to forecast outbreaks in real-time. We will trace the evolution of disease tracking as it shifts from \u0022old school\u0022 statistics to the cutting-edge AI architectures currently protecting our communities. Join us to see how these high-tech tools empower public health leaders to make faster, smarter decisions when every second counts.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Move beyond slow, traditional reporting and explore how modern AI uses everyday data, like internet queries and sales records, to forecast outbreaks in real-time."}],"uid":"27233","created_gmt":"2026-04-16 12:25:40","changed_gmt":"2026-04-16 18:19:29","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2026-05-04T10:00:00-04:00","event_time_end":"2026-05-04T11:00:00-04:00","event_time_end_last":"2026-05-04T11:00:00-04:00","gmt_time_start":"2026-05-04 14:00:00","gmt_time_end":"2026-05-04 15:00:00","gmt_time_end_last":"2026-05-04 15:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"hg_media":{"679968":{"id":"679968","type":"image","title":"20260504_CHHS_webinar.jpg","body":null,"created":"1776344727","gmt_created":"2026-04-16 13:05:27","changed":"1776344727","gmt_changed":"2026-04-16 13:05:27","alt":"CHHS Webinar Series: \u0022Infectious Disease Forecasting with Digital Data Streams: Comparing AI Transformers with Classical Statistical Methods\u0022","file":{"fid":"264195","name":"20260504_CHHS_webinar.jpg","image_path":"\/sites\/default\/files\/2026\/04\/16\/20260504_CHHS_webinar.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/04\/16\/20260504_CHHS_webinar.jpg","mime":"image\/jpeg","size":164931,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/04\/16\/20260504_CHHS_webinar.jpg?itok=zTIOmqaJ"}}},"media_ids":["679968"],"related_files":{"264196":{"fid":"264196","name":"CHHS Webinar Series flyer: \u0022Infectious Disease Forecasting with Digital Data Streams: Comparing AI Transformers with Classical Statistical Methods\u0022","file_path":"\/sites\/default\/files\/documents\/2026-04\/GTCHHS-WebinarSeries_ShihaoYang_20260504.pdf","file_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/documents\/2026-04\/GTCHHS-WebinarSeries_ShihaoYang_20260504.pdf","mime":"application\/pdf","description":""}},"related_links":[{"url":"https:\/\/gatech.zoom.us\/webinar\/register\/WN_rGkOsJm9QLq2T4KXVZ1Mcw","title":"To attend, please register online via Zoom"},{"url":"https:\/\/chhs.gatech.edu\/sites\/default\/files\/downloads\/GTCHHS-WebinarSeries_ShihaoYang_20260504.pdf","title":"Download the seminar flyer"}],"groups":[{"id":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"194684","name":"Free"},{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}