Department of Physics, Universitat Politécnica de Catalunya, Barcelona, Spain.
Spanish Ministry of Transport, Mobility and Urban Agenda (MITMA), Madrid, Spain.
Front Public Health. 2024 Mar 7;12:1288531. doi: 10.3389/fpubh.2024.1288531. eCollection 2024.
We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions.
We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth.
We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series.
Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.
我们利用 2020 年 8 月至 2021 年 3 月的西班牙数据作为自然实验，分析在疫苗接种前，即易感性水平较高，且在非药物干预的地理上不对称实施期间，一种标准化的 COVID-19 增长衡量标准如何与 48 个西班牙省份的不对称气象和流动情况相关。我们从不同的公共来源开发可靠的聚合流动数据，并从卫星数据中计算每个西班牙省份的平均温度、露点和紫外线辐射的气象时间序列。我们使用主成分分析对数据进行降维，并研究流动和气象数据与 COVID-19 增长的单变量和多变量相关性。
我们发现，在一些省份，但不是所有省份，工作日聚合流动的单变量相关性具有显著但通常较弱的相关性。另一方面，主成分分析表明，不同的流动时间序列可以适当简化为三个时间序列。对 COVID-19 增长率与这三个时间序列的多元时间滞后正则相关分析显示出高度显著的相关性，中位数 R-squared 为 0.65。气象数据与 COVID-19 增长之间的单变量相关性通常不显著，但将其两个主要主成分添加到移动多元分析中，会显著增加相关性，在所有省份的相关系数在 0.6 到 0.98 之间，中位数 R-squared 为 0.85。这一结果在数据序列降维的不同方法中具有稳健性。
我们的结果表明，流动对 COVID-19 病例增长率有重要影响。这种影响在气象变量中通常观察不到，但在一些西班牙省份，它可能变得相关。流动与增长率之间的相关性在 2-3 周的时间延迟时达到最大值，这与感染、症状发展和病例检测/报告之间预期的 5-10 天延迟相符。
