High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County

ABSTRACT Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling...

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Autores principales:	Smruthi Karthikeyan, Nancy Ronquillo, Pedro Belda-Ferre, Destiny Alvarado, Tara Javidi, Christopher A. Longhurst, Rob Knight
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	COVID-19 SARS-CoV-2 epidemiology wastewater Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/7c713cc0f53940ecbf21c64f9aeb318c
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spelling	oai:doaj.org-article:7c713cc0f53940ecbf21c64f9aeb318c2021-12-02T19:22:27ZHigh-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County10.1128/mSystems.00045-212379-5077https://doaj.org/article/7c713cc0f53940ecbf21c64f9aeb318c2021-04-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00045-21https://doaj.org/toc/2379-5077ABSTRACT Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates. IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.Smruthi KarthikeyanNancy RonquilloPedro Belda-FerreDestiny AlvaradoTara JavidiChristopher A. LonghurstRob KnightAmerican Society for MicrobiologyarticleCOVID-19SARS-CoV-2epidemiologywastewaterMicrobiologyQR1-502ENmSystems, Vol 6, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 SARS-CoV-2 epidemiology wastewater Microbiology QR1-502
spellingShingle	COVID-19 SARS-CoV-2 epidemiology wastewater Microbiology QR1-502 Smruthi Karthikeyan Nancy Ronquillo Pedro Belda-Ferre Destiny Alvarado Tara Javidi Christopher A. Longhurst Rob Knight High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
description	ABSTRACT Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates. IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.
format	article
author	Smruthi Karthikeyan Nancy Ronquillo Pedro Belda-Ferre Destiny Alvarado Tara Javidi Christopher A. Longhurst Rob Knight
author_facet	Smruthi Karthikeyan Nancy Ronquillo Pedro Belda-Ferre Destiny Alvarado Tara Javidi Christopher A. Longhurst Rob Knight
author_sort	Smruthi Karthikeyan
title	High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
title_short	High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
title_full	High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
title_fullStr	High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
title_full_unstemmed	High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County
title_sort	high-throughput wastewater sars-cov-2 detection enables forecasting of community infection dynamics in san diego county
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/7c713cc0f53940ecbf21c64f9aeb318c
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High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County

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