The Similarities and Distances of Growth Rates Related to COVID-19 Between Different Countries Based on Spectral Analysis - Chen, Ray-Ming (Frontiers in Public Health, 2021)
COVID-19 pandemic has taken more than 1.78 million of lives across the globe. To identify the underlying evolutive patterns between different countries would help us single out the mutated paths and behaviour of this virus. We devise an orthonormal basis which would serve the features to relate the evolution of one country's cases and deaths to others via coefficients from the inner product. Then we rank the coefficients measured by the inner product via the featured frequencies. The distances between these ranked vectors are evaluated by Manhattan metric. Afterwards, we associate each country with its nearest neighbour which shares the evolutive pattern via the distance matrix. Our research shows such pattern is not random at all, i.e., the underlying pattern could be contributed to some factors. In the end, we perform the typical cosine similarity on the time-series data. The comparison shows our mechanism differs from the typical one, but also related to each other in some way. There findings reveal the underlying interaction between countries with respect to cases and deaths of COVID-19.