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 and Data Science, Yale University, New Haven, CT, USA.
Sci Adv. 2022 Jan 7;8(1):eabi5499. doi: 10.1126/sciadv.abi5499.
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.
人与人之间的密切接触是导致2019冠状病毒病（COVID-19）的严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的主要传播途径。我们使用移动设备地理位置数据在人群层面量化了人际接触情况。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（6英尺范围内）的接触频率，并按居住区域汇总了接触事件的数量。当将这些数据纳入COVID-19传播的SEIR型模型时，接触率准确预测了康涅狄格州各城镇的COVID-19病例。康涅狄格州的接触情况解释了3月至4月的首轮感染、6月至8月病例的下降、8月至9月的局部疫情爆发、9月至12月全州范围内的广泛疫情反弹以及2021年1月病例的减少。与其他流动性指标相比，使用接触率的传播模型能更好地拟合COVID-19的传播动态。接触率数据有助于指导社交距离措施和检测资源的分配。
