Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.

FEMS microbes Pub Date : 2022-01-10 eCollection Date: 2021-01-01 DOI:10.1093/femsmc/xtab022
Alessandro Zulli, Annabelle Pan, Stephen M Bart, Forrest W Crawford, Edward H Kaplan, Matthew Cartter, Albert I Ko, Marcela Sanchez, Cade Brown, Duncan Cozens, Doug E Brackney, Jordan Peccia
{"title":"Predicting daily COVID-19 case rates from SARS-CoV-2 RNA concentrations across a diversity of wastewater catchments.","authors":"Alessandro Zulli, Annabelle Pan, Stephen M Bart, Forrest W Crawford, Edward H Kaplan, Matthew Cartter, Albert I Ko, Marcela Sanchez, Cade Brown, Duncan Cozens, Doug E Brackney, Jordan Peccia","doi":"10.1093/femsmc/xtab022","DOIUrl":null,"url":null,"abstract":"<p><p>We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.</p>","PeriodicalId":73024,"journal":{"name":"FEMS microbes","volume":" ","pages":"xtab022"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f7/67/xtab022.PMC8807199.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"FEMS microbes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/femsmc/xtab022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

We assessed the relationship between municipality COVID-19 case rates and SARS-CoV-2 concentrations in the primary sludge of corresponding wastewater treatment facilities. Over 1700 daily primary sludge samples were collected from six wastewater treatment facilities with catchments serving 18 cities and towns in the State of Connecticut, USA. Samples were analyzed for SARS-CoV-2 RNA concentrations during a 10 month time period that overlapped with October 2020 and winter/spring 2021 COVID-19 outbreaks in each municipality. We fit lagged regression models to estimate reported case rates in the six municipalities from SARS-CoV-2 RNA concentrations collected daily from corresponding wastewater treatment facilities. Results demonstrate the ability of SARS-CoV-2 RNA concentrations in primary sludge to estimate COVID-19 reported case rates across treatment facilities and wastewater catchments, with coverage probabilities ranging from 0.94 to 0.96. Lags of 0 to 1 days resulted in the greatest predictive power for the model. Leave-one-out cross validation suggests that the model can be broadly applied to wastewater catchments that range in more than one order of magnitude in population served. The close relationship between case rates and SARS-CoV-2 concentrations demonstrates the utility of using primary sludge samples for monitoring COVID-19 outbreak dynamics. Estimating case rates from wastewater data can be useful in locations with limited testing availability, testing disparities, or delays in individual COVID-19 testing programs.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从不同污水集水区的 SARS-CoV-2 RNA 浓度预测 COVID-19 的日发病率。
我们评估了城市 COVID-19 病例率与相应污水处理设施初级污泥中 SARS-CoV-2 浓度之间的关系。我们从为美国康涅狄格州 18 个城镇提供服务的六个污水处理设施收集了 1700 多份每日初级污泥样本。对样本进行了 SARS-CoV-2 RNA 浓度分析,分析时间跨度为 10 个月,与 2020 年 10 月和 2021 年冬春 COVID-19 在各市的爆发时间重叠。我们根据每天从相应的污水处理设施中收集的 SARS-CoV-2 RNA 浓度拟合了滞后回归模型,以估计六个城市的报告病例率。结果表明,初级污泥中的 SARS-CoV-2 RNA 浓度能够估算出各处理设施和污水集水区的 COVID-19 报告病例率,覆盖概率在 0.94 到 0.96 之间。滞后期为 0 到 1 天的模型预测能力最强。留空交叉验证表明,该模型可广泛应用于服务人口超过一个数量级的污水集水区。病例发生率与 SARS-CoV-2 浓度之间的密切关系表明,使用原生污泥样本监测 COVID-19 的爆发动态是非常有用的。从废水数据中估计病例发生率对于检测能力有限、检测不均衡或个别 COVID-19 检测项目延迟的地区非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.30
自引率
0.00%
发文量
0
审稿时长
15 weeks
期刊最新文献
Evaluating the impact of redox potential on the growth capacity of anaerobic gut fungi. Contact with young children is a major risk factor for pneumococcal colonization in older adults. Trivalent immunization with metal-binding proteins confers protection against enterococci in a mouse infection model. Arginine impacts aggregation, biofilm formation, and antibiotic susceptibility in Enterococcus faecalis. Pandemic storytelling and student engagement: how students imagined pandemics before COVID-19 pandemic.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1