Since its isolation in Wuhan SARS-Cov2 showed a high mutation rate hindering the ability to properly characterize. Also as a consequence of its size, traditional sequence analysis methods were computationally constrained. However, applying variational autoencoders (VAEs) to a custom sequence representation results in a series of clusters sorted by the sunshine duration (SD) rate of change (SDRC). The transition between clusters is characterized by changes in viral genome size, apparent deletions can be found throughout the SARS-Cov2 genome. This series of deletions might behave as an internal clock inside the genome. Using SD-derived features as a time scale results in synchronizing COVID-19 cases into a single period. Both SD-derived features and solar features correlate with COVID-19 cases, except for wavelengths at the SWIR band, pointing towards a solar-dependent seasonality. Further development of analysis techniques will help us to better understand the seasonality and adaptation of pathogenic organisms.