Application of Cross-Wavelet and Singular Value Decomposition on Covid-19 and Bio-Physical Data

Abstract

The paper examines the bivariate relationship between COVID-19 and temperature time series using Singular Value Decomposition (SVD) and continuous cross-wavelet analysis. The COVID-19 incidence data and the temperature data of the corresponding period were transformed using SVD into significant eigen-state vectors for each spatial unit. Wavelet transformation was performed to analyze and compare the frequency structure of the single and the bivariate time series. The result provides coherency measures in the ranges of time period for the corresponding spatial units. Additionally, wavelet power spectrum and paired wavelet coherence statistics and phase difference were estimated. The result suggests statistically significant coherency at various frequencies. It also indicates complex conjugate dynamic relationships in terms phases and phase differences.