SARS-Cov2 seasonality and adaptation are driven by solar activity.

Abstract

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 custom sequence representations results in a series of clusters sorted by the sunshine duration (SD) rate of change (SDRC) and other solar-derived features. 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. SDRC-derived features synchronize COVID-19 cases into a single period. Both SDRC-derived features and solar features correlate with COVID-19 cases pointing towards a solar-dependent seasonality. Atmospheric changes that affect solar radiation also showed a correlation with COVID-19 cases. Analyzing viral genome composition as time series displays an attractor-like behavior under different solar-derived time scales. While clustering them by environmental conditions shows a similar pattern as the one found by the VAE models. Further development of analysis techniques will help us to better understand the seasonality and adaptation of pathogenic organisms.