{"id":3037,"date":"2023-01-22T09:59:31","date_gmt":"2023-01-22T17:59:31","guid":{"rendered":"https:\/\/colleges.claremont.edu\/ccms\/?post_type=tribe_events&#038;p=3037"},"modified":"2023-01-31T06:58:26","modified_gmt":"2023-01-31T14:58:26","slug":"prof-joan-ponce","status":"publish","type":"tribe_events","link":"https:\/\/colleges.claremont.edu\/ccms\/event\/prof-joan-ponce\/","title":{"rendered":"Building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City (Prof. Joan Ponce, Arizona State University)"},"content":{"rendered":"<div>\n<div>\n<div dir=\"ltr\"><strong>Title:<\/strong> Building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City<\/div>\n<\/div>\n<\/div>\n<div dir=\"ltr\"><\/div>\n<div dir=\"ltr\"><strong>Speaker:<\/strong> Joan Ponce, Department of Mathematics, Arizona State University<\/div>\n<div dir=\"ltr\"><\/div>\n<div dir=\"ltr\"><strong>Abstract:<\/strong> Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics.\u00a0We develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow\u00a0that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios.\u00a0 In particular, we apply this framework to propose a modified susceptible\u2013exposed\u2013infectious\u2013recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC\u2019s government\u2019s website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Title: Building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City Speaker: Joan Ponce, Department of Mathematics, Arizona State University Abstract: Epidemiological models can provide the [&hellip;]<\/p>\n","protected":false},"author":202,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":"","_tec_slr_enabled":"","_tec_slr_layout":""},"tags":[],"tribe_events_cat":[12],"class_list":["post-3037","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-colloquium","cat_colloquium"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - 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