Epi-Clock: A sensitive platform to help understand pathogenic disease outbreaks and facilitate the response to future outbreaks of concern.

To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the novel ZHU algorithm to different SARS-CoV-2 datasets before outbreaks to search for significant mutational accumulation patterns correlated with outbreak events. Surprisingly, some inter-species genetic distances in Coronaviridae may represent intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The insertions and deletions in whole-genome sequences between different hosts were separately associated with important roles in host transmission and shifts in Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play a dominant role in the divergence of different lineages of SARS-CoV-2 in different regions of the world owing to the lack of nucleosome protection. We suggest that strong selective variation among different lineages of SARS-CoV-2 is required to produce strong codon usage bias, which appears in B.1.640.2 and B.1.617.2 (Delta). Notably, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, accumulated, especially in the pre-breakout phase, although some of the previous substitutions were replaced by other dominant genotypes. From most validations, we could accurately predict the potential pre-phase of outbreaks with a median interval of 5 days.