Several reports have highlighted the contributions of host factors such as age, gender and co-morbidities such as diabetes, hypertension and coronary heart disease in determining COVID-19 disease severity. However, inspite of initial efforts at understanding the contributions of SARS-CoV-2 variants, most were unable to delineate causality. Hence, in this study we re-visited the contributions of different clades of viruses (G, GR and GH) along with other attributes in explaining the disparity in mortality rates among countries. A total of 26,642 high quality SARS-CoV-2 sequences were included and the A23,403G (S:D614G) variant was found to be in linkage disequilibrium with C14,408 U (RdRp: P323L). Linear regression analyses revealed increase in age [Odds ratio: 1.055 (p-value 0.000358)] and higher frequency of clade-G viruses [Odds ratio: 1.029(p-value 0.000135)] could explain 37.43% of the differences in mortality rates across the 58 countries (Multiple R-squared: 0.3743). Next, Machine-Learning algorithms LogitBoost and AdaboostM1 were applied to determine whether countries belonging to high/low mortality groups could be classified using the same attributes and accurate classification was achieved in 70.69% and 62.07% of the countries, respectively. Further, evolutionary analyses of the Indian viral population (n = 662) were carried out. Allele frequency spectrum, nucleotide diversity (π) values and negative Tajima's D values across ORFs were indicative of population expansion. Network analysis revealed the presence of two major clusters of viral haplotypes, namely, clade-G and a variant of clade L [L] having the RdRp:A97V amino acid change. Clade-G genomes were found to be evolving more rapidly and were also found in higher proportions in three states with highest mortality rates namely, Gujarat, Madhya Pradesh and West Bengal. Thus, the findings of this study and results from in vitro studies highlighting the role of these variants in increasing transmissibility and altering response to antivirals reflect the role of viral factors in disease prognosis.