Genomic Surveillance of Sars- Cov 2 to Reconstruct infection Dynamics and Phylodynamics using Phylogenetic Infrence of nepal

Abstract

Severe acute respiratory syndrome (SARS-CoV-2) have caused an unprecedented impact global public health and the economy. Both current and prospective interventions require a firm understanding of evolutionary and epidemiological parameters of novel SARS-CoV2. Phylogenetic and phylodynamic approaches have provided critical insights into the spread of SARS-CoV-2 in international level, aided in tracking virus genetic changes, allowed the investigations of outbreaks and transmission chains, and informed for public health strategy. Nepal lack any such phylodynamic study on the SARS-CoV-2 genome to infer the epidemiological evolutionary state of virus using phylogenetic networks and growth trends. This study aims to investigate and reconstruct the evolutionary and epidemiological dynamics of SARS-CoV-2 virus in Nepal using the 278 genomes of SARSCoV-2 sequenced in Biotechnology laboratory, TU. In this study, we used the Bayesian Phylodynamic pipeline and TransPhylo to analyze and evaluate the evolutionary, epidemiological and infection dynamics of SARS-CoV-2. Phylogenetic tree was inferred to study the evolution of SARS-CoV-2 variants, Reproduction number of virus and transmission chain of COVID-19 infection were estimated and the possible unsampled cases of SARS-CoV-2 was predicted. Phylogenetic analysis showed the presence of Omicron, Delta and Alpha variants from our dataset. Depending upon the Bayesian timescaled phylogenetic analysis using the best fitting model showed us that the estimated evolutionary rate of SARS-CoV-2 was 1.226×10 -3 substitutions per site per year. Estimated reproduction number showed two growing phases, one during early 2021 and other during early 2022. The mean estimated R value ranged from 0.495 to 4.8472. Similarly, inference of viral transmission chain using TransPhylo showed that inferred unsampled sources greatly outnumber the actual sequenced samples in the transmission network. Prediction of unsampled cases using the available genome sequences also suggested that very high cases are not being sequenced and are acting as unsampled source of infection. Our findings highlight the critical importance of establishing genomic surveillance programs to guarantee the current state of the epidemic and to ensure impactful decision making for the allotment of intervention initiatives against the most relevant variants. x Also, the study suggests the usefulness of various phylogenetic and phylodynamic approaches in supporting the surveillance of COVID-19 and other emerging disease outbreaks. Keywords: SARS-CoV-2, Genomic surveillance, Phylogenetic, Phylodynamic, TransPhylo, Bayesian Inference, Reproduction number, Transmission chain