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

Dados Bibliográficos

ID22067795
AutoresDavid Conesa (0000-0002-5442-5691, Universitat Politècnica de Catalunya), Víctor López de Rioja (0000-0003-4485-0616, Universitat Politècnica de Catalunya), Tania Gullón (Ministerio de Transportes, Movilidad y Agenda Urbana), Adrià Tauste Campo (0000-0003-0982-4017, Universitat Politècnica de Catalunya), Clara Prats (0000-0002-1398-7559, Universitat Politècnica de Catalunya), Enrique Alvarez-Lacalle (0000-0001-6824-6857, Universitat Politècnica de Catalunya), Blas Echebarria (0000-0003-0503-1781, Universitat Politècnica de Catalunya, autor correspondente)
Ano2024
Volume12
Páginas1288531-1288531
Data de publicação2024-03-07
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1288531
PMID38528860
OpenAlexW4392558299
IdiomaEN
Citações recebidas1
Referências citadas43

Introduction: 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. Methods: 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. Results: 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. Discussion: 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

2019-20 coronavirus outbreak · Correlation · Disease · Environmental health · Outbreak · Pathology · Population · Statistics · COVID-19 Clinical Research Studies · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Mathematics · Medicine · Virology