Fig 1.
Image illustrates the key factors discussed in this study that may influence viral evolution, including different age, geographic regions of India, and vaccination efforts.
Figure created with licensed version of Biorender.com.
Table 1.
Distribution of samples by age group and gender.
Table 2.
Kolmogorov-Smirnov test results for temporal distribution.
Fig 2.
Temporal Distribution of Samples used in the study Across the Age Groups.
It captures the time period between March 2020 to August 2023 of the COVID-19 cases in India.
Table 3.
Pairwise comparisons of age groups using chi-square statistics.
Fig 3.
The plot illustrates the association Between SARS-CoV-2 Lineages and Age Groups Based on Observed Number of Substitutions.
The x-axis represents the different viral lineages, while the y-axis, transformed logarithmically, displays the count of unique substitutions associated with each lineage.
Fig 4.
Distribution of normalized count of unique substitutions across different age groups and genders in various Indian states.
Bar plots representing this distribution is summarized in this figure.
Fig 5.
Map illustrates the distribution of normalized count of unique substitutions across Indian states.
Each state color is based on the number of unique substitutions found within its population. This figure is created using shapefile data sourced from DataMeet GitHub repository: [https://github.com/datameet/maps/tree/master/States]. Shapefile data is licensed under MIT License (2020).
Fig 6.
Heatmap illustrating the distribution of unique substitution ratio across different age groups and Indian states.
Each state is represented by a row, and the columns correspond to age groups: children (1–17), working-age adults (18–64), and elderly (65+).
Fig 7.
The bar plot illustrates the substitution frequency across various locations in India, segmented by three different time periods: before vaccination, during vaccination, and after vaccination.
The data is further divided into three age groups: Children (1-17 years), Elderly (65+ years), and Working-age individuals (18-64 years), represented by green, blue, and orange bars, respectively. Each location shows the cumulative substitution frequency, with distinct patterns observed in different periods. This visual representation underscores the variations in substitution frequency by age group and location, influenced by the vaccination timeline.
Fig 8.
Dynamic changes in unique substitution frequencies influenced by the vaccination status and different states of India.
The plot captures the profile vis-à-vis the different milestone of the COVID-19 footprint in India.
Fig 9.
Substitution distribution plot across the SARS-CoV-2 genome, with substitutions categorized by age groups: Children (1–17), Working-age individuals (18–64), and Elderly adults (65+).
Substitutions are plotted at specific positions on genes, with the Y-axis representing the number of samples in which unique substitution were found present.
Table 4.
Standardized synonymous and non-synonymous substitution counts across genes in different groups.