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We use Direct Coupling Analysis (DCA) to determine epistatic interactionsbetween loci of variability of the SARS-CoV-2 virus, segmenting genomes bymonth of sampling. We use full-length, high-quality genomes from the GISAIDrepository up to October 2021, in total over 3,500,000 genomes. We find thatDCA terms are more stable over time than correlations, but nevertheless changeover time as mutations disappear from the global population or reach fixation.Correlations are enriched for phylogenetic effects, and in particularlystatistical dependencies at short genomic distances, while DCA brings out linksat longer genomic distance. We discuss the validity of a DCA analysis underthese conditions in terms of a transient Quasi-Linkage Equilibrium state. Weidentify putative epistatic interaction mutations involving loci in Spike.

Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences