SARS-Cov2 seasonality and adaptation are driven by solar activity.
Authors: Gonzalez-Lugo Octavio 
Published: August 2023

Since its isolation, SARS-Cov2 showed a high mutation rate hindering the ability to properly characterize it. Also as a consequence of its size, traditional sequence analysis methods are computationally constrained.
Nonetheless, machine learning technologies and particularly deep learning offer a viable and scalable solution to the analysis. Applying variational autoencoders (VAEs), a special kind of neural network used for unsupervised learning, to custom genomic sequence representations cluster the genomes by similarity. The resulting learned representation sorts the SARS-Cov2 genomes 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. Different deletions can be found throughout the SARS-Cov2 genome. Deletions might behave as an internal clock inside the genome. Or as an adaptation mechanism to seasonal variations in solar radiation. As genome adaptation follows SDRC COVID-19 cases could also follow the same pattern. However, the spherical geometry of our planet adds a confounding variable to the analysis. Spherical correction by dynamic fractional differentiation of the SD synchronizes COVID-19 cases into a single period. This analysis results in a general coordinate system that shows the seasonality of the disease and allows us to analyze the impact of other environmental features. Atmospheric changes that also affect solar radiation such as the ozone column and cloudiness gradients also showed a correlation with COVID-19 cases.
Analyzing viral genome composition (amount of each specific nucleotide) as time series displays an attractor-like behavior under different solar-derived time scales. Oscillations in genome composition could explain the changes in the efficacy of different antiviral treatments through the pandemic. To further characterize and expand the prediction capability of the model a set of solar and environmental features are added as targets in a supervised learning task. Allowing to correlate environmental changes and genomic composition.
Genome sorting is obtained in a two-dimensional learned representation and appears that only one dimension encodes a temporal variable. The learned representation could also show the interaction between the host or a particular tissue of the host and the virus. To further characterize the learned representation the VAE model is modified to also fit a predator-prey model using the learned representation as observations of the model. Retrieved dynamics shows a transitory phase with a duration similar to the SARS-Cov2 incubation time. However, further characterization is needed to accurately interpret those results.
Under a general coordinate system, the different mutations or genomic adaptations follow a regular and deterministic pattern. If the adaptations are the result or the means to copy the codon usage bias(CUB) of the host it is likely that SARS-Cov2 follows the expression of compositionally similar genes. Selection of highly compositionally similar transcripts results in the isolation of a series of genes whose dysregulation matches some of the symptoms reported in post-acute COVID-19 sequelae or long Covid. Also, genes that regulate the circadian rhythm and that are highly expressed at periods of high COVID-19 prevalence. Copying or optimizing genomic composition towards the host's CUB will increase the probability of the virus to copy a significant fragment of a host protein leading to autoimmunity. Dysregulation of the solar-based regulation of the circadian rhythm could explain the variability and random-like behavior of the symptoms in the post acute phase.
Further development of analysis techniques will help us to better understand the seasonality and adaptation of pathogenic organisms.