Publications by authors named "Unnati Bhalerao"

Article Synopsis
  • Tracking and monitoring SARS-CoV-2 variants has become complex due to reduced clinical testing, prompting a study of wastewater-based epidemiology (WBE) in Pune, India, to enhance variant surveillance.
  • Over 1,100 wastewater samples were analyzed, revealing "silent waves" of high viral load that occurred before clinical cases, indicating possible hidden transmission and allowing early detection of variants like XBB up to 253 days before they appeared in clinical data.
  • The study demonstrated that WBE captures a wider variety of circulating variants, providing valuable insights for public health officials to better manage and respond to potential future COVID-19 waves amidst decreasing clinical testing.
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Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach.

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The COVID-19 pandemic has emphasized the urgency for rapid public health surveillance methods to detect and monitor the transmission of infectious diseases. The wastewater-based epidemiology (WBE) has emerged as a promising tool for proactive analysis and quantification of infectious pathogens within a population before clinical cases emerge. In the present study, we aimed to assess the trend and dynamics of SARS-CoV-2 variants using a longitudinal approach.

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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.

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