Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.
Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data.
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密切的人际接触对 COVID-19 发病率的影响：来自一年移动设备数据的证据。
DOI：
10.1101/2021.03.10.21253282
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Morozova,Olga
中科院分区：
文献类型：
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作者：
Crawford,ForrestW;Jones,SydneyA;Cartter,Matthew;Dean,SamanthaG;Warren,JoshuaL;Li,ZehangRichard;Barbieri,Jacqueline;Campbell,Jared;Kenney,Patrick;Valleau,Thomas;Morozova,Olga
人与人之间的密切接触是导致2019冠状病毒病（COVID-19）的病毒SARS-CoV-2的主要传播途径。我们使用移动设备的地理定位数据量化了人口水平上的人际接触。我们计算了2020年2月至2021年1月期间康涅狄格州人们之间（6英尺内）的接触频率，并按居住地区汇总了接触事件的计数。当将接触率纳入COVID-19传播的seir型模型时，可以准确预测康涅狄格州城镇的COVID-19病例。康涅狄格州的接触解释了3月至4月期间感染的最初浪潮，6月至8月期间病例下降，8月至9月期间局部爆发，9月至12月期间全州范围内的复苏，以及2021年1月的下降。与其他流动性指标相比，使用接触率的传播模型更符合COVID-19的传播动态。接触率数据有助于指导保持社交距离和检测资源分配。
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.