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

Abstract

Abstract To predict potential epidemic outbreaks, we tested our strategy, Epi-Clock, which applies the novel ZHU algorithm on different SARS-CoV-2 datasets before outbreaks to search for real significant mutational accumulation patterns correlated with the outbreak events. Surprisingly, some inter-species genetic distances of Coronaviridae may represent the intermediate states of different species or subspecies in the evolutionary history of Coronaviridae. The insertions and deletions of whole genome sequences between different hosts were separately associated with important roles in the host transmission and shifts of Coronaviridae. Furthermore, we believe that non-nucleosomal DNA may play dominant roles in the divergence of different lineages of SARS-CoV-2 in different regions of the world because of 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, significantly appear in B.1.640.2 and B.1.617.2 (Delta). Interestingly, we found that an increasing number of other types of substitutions, such as those resulting from the hitchhiking effect, have accumulated, especially in the pre-breakout phase, even though some 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 before.