{"id":7753,"date":"2020-06-23T12:26:12","date_gmt":"2020-06-23T19:26:12","guid":{"rendered":"https:\/\/depts.washington.edu\/pandemicalliance\/?p=7753"},"modified":"2021-03-26T12:27:15","modified_gmt":"2021-03-26T19:27:15","slug":"characterizing-super-spreading-events-and-age-specific-infectivity-of-covid-19-transmission-in-georgia-usa-2","status":"publish","type":"post","link":"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/06\/23\/characterizing-super-spreading-events-and-age-specific-infectivity-of-covid-19-transmission-in-georgia-usa-2\/","title":{"rendered":"Characterizing Super-Spreading Events and Age-Specific Infectivity of COVID-19 Transmission in Georgia USA"},"content":{"rendered":"<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"43\" data-aria-posinset=\"1\" data-aria-level=\"1\"><i><span data-contrast=\"none\">[pre-print, not peer reviewed]<\/span><\/i><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">Lau et al. use surveillance, geolocation, and aggregate mobility\u00a0<\/span><span data-contrast=\"auto\">data from<\/span><span data-contrast=\"auto\">\u00a0five counties in Georgia (US) to estimate unobserved parameters, including date of infection and transmission pathway. They estimate R<\/span><span data-contrast=\"auto\">0<\/span><span data-contrast=\"auto\">\u00a0to be 2.88 (95%C<\/span><span data-contrast=\"auto\">I<\/span><span data-contrast=\"auto\">\u00a01.85, 4.9) before a state-wide shelter-in-place order, and &lt;1 two weeks after the order.<\/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=\"43\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">They estimate that 2% of cases may have resulted in 20% of infections<\/span><span data-contrast=\"auto\">, suggesting the presence of super<\/span><span data-contrast=\"auto\">&#8211;<\/span><span data-contrast=\"auto\">spreading events<\/span><span data-contrast=\"auto\">, with\u00a0<\/span><span data-contrast=\"auto\">those younger than 60 years of age\u00a0<\/span><span data-contrast=\"auto\">more than twice as likely to transmit as\u00a0<\/span><span data-contrast=\"auto\">those\u00a0<\/span><span data-contrast=\"auto\">60<\/span><span data-contrast=\"auto\">\u00a0years<\/span><span data-contrast=\"auto\">\u00a0or older.<\/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\">Lau et al. (June 22, 2020). Characterizing Super-Spreading Events and Age-Specific Infectivity of COVID-19 Transmission in Georgia USA.\u00a0<\/span><\/i><i><span data-contrast=\"none\">Preprint\u00a0downloaded\u00a0June 23 from\u00a0<\/span><\/i><i><span data-contrast=\"none\">\u00a0<\/span><\/i><a href=\"https:\/\/doi.org\/10.1101\/2020.06.20.20130476\"><span data-contrast=\"none\">https:\/\/doi.org\/10.1101\/2020.06.20.20130476<\/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]\u00a0Lau et al. use surveillance, geolocation, and aggregate mobility\u00a0data from\u00a0five counties in Georgia (US) to estimate unobserved parameters, including date of infection and transmission pathway. They estimate R0\u00a0to be 2.88 (95%CI\u00a01.85, 4.9) before a state-wide shelter-in-place order, and &lt;1 two weeks after the order.\u00a0 They estimate that 2% of cases may have&#8230;<\/p>\n<div><a class=\"more\" href=\"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/06\/23\/characterizing-super-spreading-events-and-age-specific-infectivity-of-covid-19-transmission-in-georgia-usa-2\/\">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-7753","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\/7753","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=7753"}],"version-history":[{"count":1,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7753\/revisions"}],"predecessor-version":[{"id":7754,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7753\/revisions\/7754"}],"wp:attachment":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/media?parent=7753"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/categories?post=7753"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/tags?post=7753"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/topic?post=7753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}