Social distancing policies are currently the best method of mitigating the spread of the COVID-19 pandemic. However, adherence to these policies vary greatly on a county-by-county level. We used social distancing adherence (SoDA) estimated from mobile phone data and population-based demographics/statistics of 3054 counties in the United States to determine which demographics features correlate to adherence on a countywide level. SoDA scores per day were extracted from mobile phone data and aggregated from March 16, 2020 to April 14, 2020. 45 predictor features were evaluated using univariable regression to determine their level of correlation with SoDA. These 45 features were then used to form a SoDA prediction model. Persons who work from home prior to the COVID-19 pandemic (β = 0.259, p < 0.00001) and owner-occupied housing unit rate (β = −0.322, p < 0.00001) were the most positively correlated and negatively correlated features to SoDA, respectively. Counties with higher per capita income, older persons, and more suburban areas were positively associated with adherence while counties with higher African American population, high obesity rate, earlier first COVID-19 case/death, and more Republican-leaning residents were negatively correlated with adherence. The base model predicted county SoDA with 90.8% accuracy. The model using only COVID-19-related features predicted with 64% accuracy and the model using the top 25 most substantial features predicted with 89% accuracy. Our results indicate that economic features, health features, and a few other features, such as political affiliation, race, and the time since the first case/death, impact SoDA on a countywide level. These features, combined, can predict adherence with a high level of confidence. Our prediction model could be utilized to inform health policy planning and potential interventions in areas with lower adherence.

当前，社会疏远政策是缓解COVID-19大流行蔓延的最佳方法。但是，各个县对这些政策的遵守情况差异很大。我们使用了根据移动电话数据和美国3054个县的基于人口的人口统计数据而估算出的社会距离依从性（SoDA），以确定哪些人口统计特征与全县范围内的依从性相关。每天从移动电话数据中提取SoDA得分，并将其从2020年3月16日到2020年4月14日汇总。使用单变量回归评估45个预测指标特征，以确定其与SoDA的相关程度。然后使用这45个特征来形成SoDA预测模型。在COVID-19大流行之前在家工作的人（β  = 0.259，p <0.00001）和业主自用住房单价（β  = −0.322，p <0.00001）分别是与SoDA的最正相关和负相关的特征。人均收入较高，老年人较多和郊区较多的县与依从性呈正相关，而非裔美国人人口较高，肥胖率较高，较早的首次COVID-19病例/死亡以及更多共和党人的居民与该州呈负相关。坚持。基本模型以90.8％的准确度预测县SoDA。仅使用预测精度为64％的COVID-19相关特征的模型，使用预测精度为89％的前25个最重要特征的模型。我们的结果表明，经济特征，健康特征以及其他一些特征（例如，从属关系，种族和自第一次病例/死亡以来的时间）在全县范围内对SoDA产生了影响。这些功能 结合在一起，可以高度自信地预测依从性。我们的预测模型可用于为依从性较低的地区提供卫生政策规划和潜在干预措施的信息。