Crawford Forrest W, Jones Sydney A, Cartter Matthew, Dean Samantha G, Warren Joshua L, Li Zehang Richard, Barbieri Jacqueline, Campbell Jared, Kenney Patrick, Valleau Thomas, Morozova Olga
Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Department of Statistics & Data Science, Yale University, New Haven, CT, USA.
medRxiv. 2021 Mar 12:2021.03.10.21253282. doi: 10.1101/2021.03.10.21253282.
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation.
Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.
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。我们试图通过使用匿名移动设备地理位置数据来量化人群层面的人际接触情况。我们计算了2020年2月至2021年1月期间康涅狄格州居民之间（距离在六英尺以内）的接触频率。然后，我们按居住地区汇总接触事件的计数，以估算每个城镇居民每天经历的人际接触总强度。当将其纳入COVID-19传播的易感-暴露-感染-康复（SEIR）模型时，接触率准确预测了该时间段内康涅狄格州各城镇的COVID-19病例。康涅狄格州的接触率模式解释了3月至4月期间最初的大规模感染浪潮、6月至8月期间病例的随后下降、8月至9月期间的局部疫情爆发、9月至12月期间全州范围内的广泛反弹以及2021年1月的下降。接触率数据有助于指导公共卫生宣传活动，鼓励保持社交距离，并有助于分配检测资源，以便比传统病例调查更快地发现或预防新出现的局部疫情爆发。
利用移动设备位置数据测量的密切人际接触解释了大流行第一年康涅狄格州COVID-19传播的动态情况。
