The modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Genomic surveillance has come to the forefront during the coronavirus disease 2019 (COVID-19) pandemic at both local and global scales to identify variants of concern. Tracking variants of concern (VOC) is integral to understanding the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in space and time. Combining phylogenetics with epidemiological data like case incidence, spatial spread, and transmission dynamics generates actionable information. Here we discuss the genome surveillance done in Pune, India, through sequencing 10,496 samples from infected individuals and integrating them with multiple heterogeneous outbreak data. The rise and fall of VOCs along with shifting transmission dynamics in the time interval of December 2020 to March 2022 was identified. Population-based estimates of the proportion of circulating variants indicated the second and third peak of infection in Pune to be driven by VOCs Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) respectively. Integrating single nucleotide polymorphism changes across all sequenced genomes identified C (Cytosine) > T (Thymine) and G (Guanine) > T (Thymine) substitutions to dominate with higher rates of adaptive evolution in Spike (S), RNA-dependent RNA polymerase (RdRp), and Nucleocapsid (N) genes. Spike Protein mutational profiling during and pre-Omicron VOCs indicated differential rank ordering of high-frequency mutations in specific domains that increased the charge and binding properties of the protein. Time-resolved phylogenetic analysis of Omicron sub-lineages identified specific recombinant X lineages, XZ, XQ, and XM. BA.1 from Pune was found to be highly divergent by global sequence alignment and hierarchical clustering. Our ″band of five″ outbreak data analytics which includes the integration of five heterogeneous data types indicates that a strong surveillance system with comprehensive high-quality metadata was critical to understand the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. We anticipate the use of such integrated workflows to be critical for pandemic preparedness in the future.
The wastewater-based epidemiology (WBE) of SARS-CoV-2 is a quick and cost-effective method of tracking virus transmission. However, few studies reported on campus or in academic or residential settings worldwide. In this study, we demonstrated the WBE approach to detect, monitor, and evaluate genomic variants of SARS-CoV-2 fragments in a sewage treatment plant (STP) located on the campus of CSIR National Chemical Laboratory, Pune, India. Herein we describe the early warning capability of WBE, with viral load rise in campus sewage water up to 14 days before its clinical detection. This was supported further by a significant correlation between SARS-CoV-2 RNA concentration and clinically reported COVID-19 cases on campus. Additionally, we comprehended the probable targets missed by the quantitative qRT-PCR using amplicon-based sequencing due to low viral load. The analysis revealed the presence of signature mutations of the Omicron (S:N679K, S:N764K, S:D796Y, N:P13L, ORF1a:T3255I, ORF1a:K856R, ORF1a:P3395H, and N:S413R) before the lineage was first detected globally. Further, we used Lineage decomposition (LCS) tool to detect the Variant of Concern (VOC)/Variant of Interest (VOI) signals upto a month earlier in sewage water samples. The analysis also indicated the transition of lineage from Delta to Omicron in late Decemeber,2021. This is the first study in India highlighting the use of on-campus STP to evaluate the local spread of SARS-CoV-2, which could aid in preventing COVID-19 in academic institutes/universities. This study proves the usefulness of WBE as an early warning system for detecting, tracking and tracing VOCs using the sequencing approach. The current study could aid in taking critical decisions to tackle the pandemic scenario on campus.HighlightsThe first study on campus sewage water for SARS-CoV-2 surveillance in IndiaEarly detection of Omicron VOC signals during early November 2021Sequencing revealed the presence of Omicron VOC fragments prior to clinical cases reported on campusGenomic analysis indicated transition of Delta to Omicron lineage in late December 2021 and potentially led to the third waveCombining qRT-PCR and sequencing could be useful for on-campus tracking of variants using wastewater surveillance
The COVID-19 pandemic has emphasized the urgency for rapid public health surveillance methods in early detection and monitoring of the transmission of infectious diseases. The wastewater-based epidemiology (WBE) has emerged as a promising tool to analyze and enumerate the prevalence of infectious pathogens in a population ahead of time. In the present study, real time quantitative polymerase chain reaction (RT-qPCR) and Illumina sequencing was performed to determine the SARS-CoV-2 load trend and dynamics of variants over a longitudinal scale in 442 wastewater (WW) samples collected from 10 sewage treatment plants (STPs) of Pune city, India, during November 2021 to April-2022. In total 426 distinct lineages representing 17 highly transmissible variants of SARS-CoV-2 were identified. The SARS-CoV-2 Omicron variant fragments were detected in WW samples prior to its detection in clinical cases. Moreover, highly contagious sub-lineages of Omicron, such as BA.2.12 (0.8-0.25%), BA.2.38 (0.8-1.0%), BA.2.75 (0.01-0.02%), BA.3 (0.09-6.3%), BA.4 (0.24-0.29%), and XBB (0.01-13.7%) fragments were significantly detected. The longitudinal analysis also suggested the presence of the BA.5 lineage in November 2021, which was not reported in the clinical settings throughout the duration of this study, indicative of silent variant persistence. Overall, the present study demonstrated the practicality of WBE in early detection of SARS CoV-2 variants, which could be useful in tracking future outbreaks of SARS-CoV-2. Such approaches could be implicated in the monitoring of the infectious agents before they appear in clinical cases.
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