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BACKGROUND

Modern response to pandemics, critical for effective public health measures, is shaped by the availability and integration of diverse epidemiological outbreak data. Tracking variants of concern (VOC) is integral to understanding the evolution of SARS-CoV-2 in space and time, both at the local level and global context. This potentially generates actionable information when integrated with epidemiological outbreak data.

METHODS

A city-wide network of researchers, clinicians, and pathology diagnostic laboratories was formed for genome surveillance of COVID-19 in Pune, India. The genomic landscapes of 10,496 sequenced samples of SARS-CoV-2 driving peaks of infection in Pune between December-2020 to March-2022, were determined. As a modern response to the pandemic, a "band of five" outbreak data analytics approach was used. This integrated the genomic data (Band 1) of the virus through molecular phylogenetics with key outbreak data including sample collection dates and case numbers (Band 2), demographics like age and gender (Band 3-4), and geospatial mapping (Band 5).

RESULTS

The transmission dynamics of VOCs in 10,496 sequenced samples identified B.1.617.2 (Delta) and BA(x) (Omicron formerly known as B.1.1.529) variants as drivers of the second and third peaks of infection in Pune. Spike Protein mutational profiling during pre and post-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 a highly divergent BA.1 from Pune in addition to recombinant X lineages, XZ, XQ, and XM.

CONCLUSIONS

The band of five outbreak data analytics approach, which integrates five different types of data, highlights the importance of a strong surveillance system with high-quality meta-data for understanding the spatiotemporal evolution of the SARS-CoV-2 genome in Pune. These findings have important implications for pandemic preparedness and could be critical tools for understanding and responding to future outbreaks.

背景

有效的公共卫生措施需要现代的疫情应对，而这取决于多样化的流行病学暴发数据的可用性和整合。追踪关注变异株（VOC）对于了解 SARS-CoV-2 在时空上的演变至关重要，无论是在地方层面还是全球背景下。当与流行病学暴发数据整合时，这可能会产生可操作的信息。

方法

在印度浦那成立了一个由研究人员、临床医生和病理学诊断实验室组成的全市范围的网络，用于对 COVID-19 进行基因组监测。确定了 2020 年 12 月至 2022 年 3 月期间推动浦那感染高峰的 10496 个 SARS-CoV-2 测序样本的基因组景观。作为对大流行的现代应对措施，采用了“五波段”暴发数据分析方法。这将病毒的基因组数据（波段 1）通过分子系统发生学与关键暴发数据（波段 2），包括样本采集日期和病例数、年龄和性别等人口统计学特征（波段 3-4）以及地理空间映射（波段 5）进行了整合。

结果

在 10496 个测序样本中，VOC 的传播动态确定了 B.1.617.2（Delta）和 BA（x）（以前称为 B.1.1.529 的 Omicron）变体是浦那第二和第三次感染高峰的驱动因素。在 Omicron VOC 之前和之后的刺突蛋白突变分析表明，在增加蛋白质电荷和结合特性的特定结构域中，高频突变的排名顺序不同。对 Omicron 亚谱系的时间分辨系统发生分析除了重组 X 谱系 XZ、XQ 和 XM 外，还确定了来自浦那的高度分化的 BA.1。