论文标题
COVID-19的地理传播与Facebook衡量的社交网络结构相关
The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook
论文作者
论文摘要
我们使用Facebook的汇总数据表明，Covid-19更可能在具有更强的社交网络连接的地区之间传播。与两个早期的Covid-19-19“热点”（纽约州威彻斯特县和意大利的Lodi省）之间有更多社交关系的地区通常在3月底之前有更多确认的Covid-19案件。这些关系在控制了与热点的地理距离以及地区密度和人口统计学之后存在。随着大流行在美国的发展，一个县与最近的Covid-19案件和死亡的社会接近预测，未来的爆发超过了身体接近和人口统计。部分由于其广泛的覆盖范围，社会连接性数据为基于智能手机位置或在线搜索数据提供的措施提供了其他预测能力。这些结果表明，来自在线社交网络的数据对流行病学家以及希望预测Covid-19等传染病的传播的其他人很有用。
We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.
