A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces - Conesa, David; López de Rioja, Víctor; Gullón, Tania; Tauste Campo, Adriá; Prats, Clara; Alvarez-Lacalle, Enrique; Echebarria, Blas (Frontiers in Public Health, 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 investigate its univariate and multivariate correlations 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. We also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. 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. We show that these results are robust to different approaches in the reduction of dimensionality of the data series. The correlation between mobility and growth rate is maximal at a time delay of two-three weeks, which agrees well with the expected five-ten day delays between infection, development of symptoms, and the detection/report of the case.