{"id":4011,"date":"2020-02-04T13:49:53","date_gmt":"2020-02-04T21:49:53","guid":{"rendered":"https:\/\/depts.washington.edu\/pandemicalliance\/?p=4011"},"modified":"2021-02-16T13:50:58","modified_gmt":"2021-02-16T21:50:58","slug":"estimating-the-risk-on-outbreak-spreading-of-2019-ncov-in-china-using-transportation-data","status":"publish","type":"post","link":"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/02\/04\/estimating-the-risk-on-outbreak-spreading-of-2019-ncov-in-china-using-transportation-data\/","title":{"rendered":"Estimating the risk on outbreak spreading of 2019-nCoV in China using transportation data"},"content":{"rendered":"<ul>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Using information from an International Air Transport Data database, SIR modeling techniques, and R<\/span><span style=\"font-weight: 400\">0<\/span><span style=\"font-weight: 400\"> estimates ranging from 1.4-2.9, critical timeframes for outbreak emergence (establishing transmission in a new locale) range from about 18-30 days. To gain 30 days in these scenarios, control measures must reduce connections between locales by 87-95%.<\/span><\/li>\n<\/ul>\n<p><i><span style=\"font-weight: 400\">Yuan HY, et al. Pre-print downloaded 4 Feb, 2020 at, <\/span><\/i><a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2020.02.01.20019984v1\"><span style=\"font-weight: 400\">https:\/\/www.medrxiv.org\/content\/10.1101\/2020.02.01.20019984v1<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Using information from an International Air Transport Data database, SIR modeling techniques, and R0 estimates ranging from 1.4-2.9, critical timeframes for outbreak emergence (establishing transmission in a new locale) range from about 18-30 days. To gain 30 days in these scenarios, control measures must reduce connections between locales by 87-95%. Yuan HY, et al. Pre-print&#8230;<\/p>\n<div><a class=\"more\" href=\"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/02\/04\/estimating-the-risk-on-outbreak-spreading-of-2019-ncov-in-china-using-transportation-data\/\">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-4011","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\/4011","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=4011"}],"version-history":[{"count":1,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/4011\/revisions"}],"predecessor-version":[{"id":4012,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/4011\/revisions\/4012"}],"wp:attachment":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/media?parent=4011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/categories?post=4011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/tags?post=4011"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/topic?post=4011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}