{"id":7108,"date":"2020-05-26T16:51:09","date_gmt":"2020-05-26T23:51:09","guid":{"rendered":"https:\/\/depts.washington.edu\/pandemicalliance\/?p=7108"},"modified":"2021-03-19T16:59:32","modified_gmt":"2021-03-19T23:59:32","slug":"visualizing-the-invisible-the-effect-of-asymptomatic-transmission-on-the-outbreak-dynamics-of-covid-19","status":"publish","type":"post","link":"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/05\/26\/visualizing-the-invisible-the-effect-of-asymptomatic-transmission-on-the-outbreak-dynamics-of-covid-19\/","title":{"rendered":"Visualizing the Invisible:\u00a0The Effect of Asymptomatic Transmission on the Outbreak Dynamics of COVID-19"},"content":{"rendered":"<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"39\" data-aria-posinset=\"1\" data-aria-level=\"1\"><i><span data-contrast=\"none\">[<\/span><\/i><i><span data-contrast=\"none\">pre-print, not peer reviewed<\/span><\/i><i><span data-contrast=\"none\">]<\/span><\/i><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">Peirlinck\u00a0et al. us<\/span><span data-contrast=\"auto\">ed<\/span><span data-contrast=\"auto\">\u00a0reported symptomatic case data, antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19 and predict R<\/span><span data-contrast=\"auto\">t<\/span><span data-contrast=\"auto\">. This approach found outbreak dynamics to be sensitive to R<\/span><span data-contrast=\"auto\">t<\/span><span data-contrast=\"auto\">, the ratio of symptomatic to asymptomatic populations, and the infectious periods of both groups.\u00a0<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"39\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">For three locations for which seroprevalence data are available, this model estimated the proportion of the population that has been infected and recovered by May 13 to be 6.2% (Santa Clara, CA), 22.7% (New York, NY) and 20.5% Heinsberg (Germany).\u00a0<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><i><span data-contrast=\"none\">Peirlinck\u00a0et al. (May 26, 2020). Visualizing the Invisible<\/span><\/i><i><span data-contrast=\"none\">:<\/span><\/i><i><span data-contrast=\"none\">\u00a0The Effect of Asymptomatic Transmission on the Outbreak Dynamics of COVID-19. Pre-print\u00a0downloaded May 26 from\u00a0<\/span><\/i><a href=\"https:\/\/doi.org\/10.1101\/2020.05.23.20111419\"><span data-contrast=\"none\">https:\/\/doi.org\/10.1101\/2020.05.23.20111419<\/span><\/a><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559685&quot;:720,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[pre-print, not peer reviewed]\u00a0Peirlinck\u00a0et al. used\u00a0reported symptomatic case data, antibody seroprevalence studies, a mathematical epidemiology model, and a Bayesian framework to infer the epidemiological characteristics of COVID-19 and predict Rt. This approach found outbreak dynamics to be sensitive to Rt, the ratio of symptomatic to asymptomatic populations, and the infectious periods of both groups.\u00a0\u00a0 For&#8230;<\/p>\n<div><a class=\"more\" href=\"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/05\/26\/visualizing-the-invisible-the-effect-of-asymptomatic-transmission-on-the-outbreak-dynamics-of-covid-19\/\">Read more<\/a><\/div>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[6],"tags":[],"topic":[23],"class_list":["post-7108","post","type-post","status-publish","format-standard","hentry","category-article-summary","topic-modeling-and-prediction"],"_links":{"self":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/comments?post=7108"}],"version-history":[{"count":1,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7108\/revisions"}],"predecessor-version":[{"id":7109,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7108\/revisions\/7109"}],"wp:attachment":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/media?parent=7108"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/categories?post=7108"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/tags?post=7108"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/topic?post=7108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}