{"id":21429,"date":"2022-01-24T10:24:29","date_gmt":"2022-01-24T15:24:29","guid":{"rendered":"https:\/\/carleton.ca\/math\/?p=21429"},"modified":"2022-01-24T11:02:38","modified_gmt":"2022-01-24T16:02:38","slug":"statistics-and-probability-letters","status":"publish","type":"post","link":"https:\/\/carleton.ca\/math\/2022\/statistics-and-probability-letters\/","title":{"rendered":"Probability and Statistics Seminar"},"content":{"rendered":"

Date & Time:\u00a0 January 28, 2022 at 2:00 p.m.
\nLocation: University of Ottawa (virtual)
\nCost:\u00a0 Free
\nSpeaker: Mahmoud Torabi (University of Manitoba)
\nTitle:\u00a0 Analysing COVID-19 Data in the Canadian Province of Manitoba: A New Approach<\/p>\n

Abstract:\u00a0 <\/strong>SEIR (susceptible-exposed-infected-removed) model is an appropriate model for <\/span>analyzing infectious diseases such as influenza and COVID-19. However, in the SEIR model, it is assumed that the population of study is homogeneous and in this model one can’t incorporate other information (e.g., location of infected people, distance between susceptible and infected individuals, risk factors) which are important in predicting e.g. COVID-19 cases.<\/span><\/p>\n

Recently, a geographically dependent individual-level model (GD-ILM) with SEIR framework was developed when spatio-temporal information and individual-level data are available. In the GD-ILM, the corresponding parameters of SEIR model (such as contact rate, incubation period, infectious period) are used for the entire population assuming the population study is homogeneous which is a basic assumption in the SEIR model. However, as we know, it is a strong assumption to assume the entire population study is homogeneous as different health regions of population study may act differently. In this talk, we propose to use a GD-ILM for each health region of Manitoba (central Canadian province) population and show it is more accurate than assuming the same SEIR model for entire population. In particular, Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters, we accurately predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas.<\/span><\/p>\n

Performance of the proposed approach is also evaluated through simulation <\/span>studies.<\/span><\/p>\n

For more information: Please contact Mahmoud Zarepour, University of Ottawa zarepour@uOttawa.ca<\/a><\/p>\n

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<\/p>\n","protected":false},"excerpt":{"rendered":"

Date & Time:\u00a0 January 28, 2022 at 2:00 p.m. Location: University of Ottawa (virtual) Cost:\u00a0 Free Speaker: Mahmoud Torabi (University of Manitoba) Title:\u00a0 Analysing COVID-19 Data in the Canadian Province of Manitoba: A New Approach Abstract:\u00a0 SEIR (susceptible-exposed-infected-removed) model is an appropriate model for analyzing infectious diseases such as influenza and COVID-19. However, in the […]<\/p>\n","protected":false},"author":18,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1,15],"tags":[],"yoast_head":"\nProbability and Statistics Seminar - School of Mathematics and Statistics<\/title>\n<meta name=\"description\" content=\"Date & Time:\u00a0 January 28, 2022 at 2:00 p.m. Location: University of Ottawa (virtual) Cost:\u00a0 Free Speaker: Mahmoud Torabi (University of Manitoba)\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/math\/2022\/statistics-and-probability-letters\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"traciebarkley\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/math\/2022\/statistics-and-probability-letters\/\",\"url\":\"https:\/\/carleton.ca\/math\/2022\/statistics-and-probability-letters\/\",\"name\":\"Probability and Statistics Seminar - 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