Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context
Summary: Background: Throughout the COVID-19 pandemic, wastewater surveillance emerged as an important tool as an important tool by providing data that are more representative of the population than case reporting, which is often biased towards individuals with health-seeking behaviour or access to...
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Elsevier
2025-08-01
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| Series: | The Lancet Regional Health - Southeast Asia |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772368225000903 |
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| author | Rebecca Fern Daniel Subash K. Kannan Namrta Daroch Sutharsan Ganesan Farhina Mozaffer Vishwanath Srikantaiah Lingadahalli Subrahmanya Shashidhara Rakesh Mishra Farah Ishtiaq |
| author_facet | Rebecca Fern Daniel Subash K. Kannan Namrta Daroch Sutharsan Ganesan Farhina Mozaffer Vishwanath Srikantaiah Lingadahalli Subrahmanya Shashidhara Rakesh Mishra Farah Ishtiaq |
| author_sort | Rebecca Fern Daniel |
| collection | DOAJ |
| description | Summary: Background: Throughout the COVID-19 pandemic, wastewater surveillance emerged as an important tool as an important tool by providing data that are more representative of the population than case reporting, which is often biased towards individuals with health-seeking behaviour or access to healthcare. With changing phases of the pandemic, decreased testing, and varying viral shedding rates, it is crucial to have a robust, sustainable, and flexible wastewater surveillance system that can serve as an independent signal of disease outbreaks. We aimed to identify ‘bellwether’ sewershed sites for sustainable disease surveillance in Bengaluru, India. Methods: We conducted this longitudinal study from December 2021 to January 2024 at 26 centralised sewershed sites in Bengaluru city (∼11 million inhabitants). We quantified weekly SARS-CoV-2 RNA concentrations to track infection dynamics and identify ‘bellwether’ sewershed sites. This was achieved by integrating established metrics for wastewater analysis, calculating sample-to-sample percentage rate of change, and applying algorithms to differentiate signal from noise, thereby validating factors contributing to the precision and reliability of outbreak predictions. Findings: Using 2873 wastewater samples, we applied a modified algorithm (COVID-SURGE algorithm) to identify ‘bellwether’ sewershed sites using longitudinal wastewater data on SARS-CoV-2 from 26 sewershed sites in Bengaluru. We utilised an Excel-based calculator (COVID-SURGE calculator) for user-entered wastewater data that differentiates signal from noise (underlying variability) based on the algorithm, with adjustments made to the input format of viral data and a specified limit of detection (LOD) value from the reverse transcriptase-quantitative PCR kit. We identified 11 ‘bellwether’ sites: four with large catchment sizes (KC Valley 1, KC Valley 2, Rajacanal, Doddabelee); four with medium sizes (Agaram, Nagasandra, KR Puram, Yelahanka); and three with small sizes (Chikkabegur, Chikkabanavara, Lalbagh). These were the best performers and can serve as a useful subset of sewage treatment plants for an early warning system at the city level. Interpretation: Using wastewater metrics helps in selecting permanent sewershed sites and identifying sub-sites that can be scaled up during peak outbreak periods to detect disease hotspots, or scaled down during lean periods, especially when clinical data are unavailable. In a post-pandemic world, particularly in low-resource settings, focusing on the best-performing sewershed sites will ensure high-quality data that captures valid signals amid the noise from wastewater, conserves resources, and optimises public health actions beyond SARS-CoV-2. Funding: This work has been supported by funding from the Rockefeller Foundation (grant 2021 HTH018) to National Centre for Biological Sciences (TIFR) and the Indian Council of Medical Research grant to (FI) Tata Institute for Genetics and Society and Tata Trusts. |
| format | Article |
| id | doaj-art-a8ecff501b444db696ad192bda78dc34 |
| institution | DOAJ |
| issn | 2772-3682 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | The Lancet Regional Health - Southeast Asia |
| spelling | doaj-art-a8ecff501b444db696ad192bda78dc342025-08-20T03:22:59ZengElsevierThe Lancet Regional Health - Southeast Asia2772-36822025-08-013910061910.1016/j.lansea.2025.100619Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in contextRebecca Fern Daniel0Subash K. Kannan1Namrta Daroch2Sutharsan Ganesan3Farhina Mozaffer4Vishwanath Srikantaiah5Lingadahalli Subrahmanya Shashidhara6Rakesh Mishra7Farah Ishtiaq8Tata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaBiome Environmental Trust, Bengaluru 560097, IndiaNational Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, IndiaTata Institute for Genetics and Society, GKVK Campus, Bellary Road, Bengaluru 560065, India; Corresponding author.Summary: Background: Throughout the COVID-19 pandemic, wastewater surveillance emerged as an important tool as an important tool by providing data that are more representative of the population than case reporting, which is often biased towards individuals with health-seeking behaviour or access to healthcare. With changing phases of the pandemic, decreased testing, and varying viral shedding rates, it is crucial to have a robust, sustainable, and flexible wastewater surveillance system that can serve as an independent signal of disease outbreaks. We aimed to identify ‘bellwether’ sewershed sites for sustainable disease surveillance in Bengaluru, India. Methods: We conducted this longitudinal study from December 2021 to January 2024 at 26 centralised sewershed sites in Bengaluru city (∼11 million inhabitants). We quantified weekly SARS-CoV-2 RNA concentrations to track infection dynamics and identify ‘bellwether’ sewershed sites. This was achieved by integrating established metrics for wastewater analysis, calculating sample-to-sample percentage rate of change, and applying algorithms to differentiate signal from noise, thereby validating factors contributing to the precision and reliability of outbreak predictions. Findings: Using 2873 wastewater samples, we applied a modified algorithm (COVID-SURGE algorithm) to identify ‘bellwether’ sewershed sites using longitudinal wastewater data on SARS-CoV-2 from 26 sewershed sites in Bengaluru. We utilised an Excel-based calculator (COVID-SURGE calculator) for user-entered wastewater data that differentiates signal from noise (underlying variability) based on the algorithm, with adjustments made to the input format of viral data and a specified limit of detection (LOD) value from the reverse transcriptase-quantitative PCR kit. We identified 11 ‘bellwether’ sites: four with large catchment sizes (KC Valley 1, KC Valley 2, Rajacanal, Doddabelee); four with medium sizes (Agaram, Nagasandra, KR Puram, Yelahanka); and three with small sizes (Chikkabegur, Chikkabanavara, Lalbagh). These were the best performers and can serve as a useful subset of sewage treatment plants for an early warning system at the city level. Interpretation: Using wastewater metrics helps in selecting permanent sewershed sites and identifying sub-sites that can be scaled up during peak outbreak periods to detect disease hotspots, or scaled down during lean periods, especially when clinical data are unavailable. In a post-pandemic world, particularly in low-resource settings, focusing on the best-performing sewershed sites will ensure high-quality data that captures valid signals amid the noise from wastewater, conserves resources, and optimises public health actions beyond SARS-CoV-2. Funding: This work has been supported by funding from the Rockefeller Foundation (grant 2021 HTH018) to National Centre for Biological Sciences (TIFR) and the Indian Council of Medical Research grant to (FI) Tata Institute for Genetics and Society and Tata Trusts.http://www.sciencedirect.com/science/article/pii/S2772368225000903Wastewater surveillanceSARS-CoV-2COVID-SURGEIndiaBengaluruCOVID-19 |
| spellingShingle | Rebecca Fern Daniel Subash K. Kannan Namrta Daroch Sutharsan Ganesan Farhina Mozaffer Vishwanath Srikantaiah Lingadahalli Subrahmanya Shashidhara Rakesh Mishra Farah Ishtiaq Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context The Lancet Regional Health - Southeast Asia Wastewater surveillance SARS-CoV-2 COVID-SURGE India Bengaluru COVID-19 |
| title | Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context |
| title_full | Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context |
| title_fullStr | Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context |
| title_full_unstemmed | Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context |
| title_short | Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context |
| title_sort | identifying bellwether sewershed sites for sustainable disease surveillance in bengaluru india a longitudinal studyresearch in context |
| topic | Wastewater surveillance SARS-CoV-2 COVID-SURGE India Bengaluru COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S2772368225000903 |
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