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Simple methods for early warnings of COVID-19 surges: Lessons learned from 21 months of wastewater and clinical data collection in Detroit, Michigan, United States.
Zhao, Liang; Zou, Yangyang; David, Randy E; Withington, Scott; McFarlane, Stacey; Faust, Russell A; Norton, John; Xagoraraki, Irene.
  • Zhao L; Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct, East Lansing, MI 48823, USA.
  • Zou Y; Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct, East Lansing, MI 48823, USA.
  • David RE; Detroit Health Department, 100 Mack Ave, Detroit, MI 48201, USA.
  • Withington S; Detroit Health Department, 100 Mack Ave, Detroit, MI 48201, USA.
  • McFarlane S; Macomb County Health Division, 43525 Elizabeth Rd, Mount Clemens, MI 48043, USA.
  • Faust RA; Oakland County Health Division, 1200 Telegraph Rd, Pontiac, MI 48341, USA.
  • Norton J; Great Lakes Water Authority, 735 Randolph, Detroit, MI 48226, USA.
  • Xagoraraki I; Department of Civil and Environmental Engineering, Michigan State University, 1449 Engineering Research Ct, East Lansing, MI 48823, USA. Electronic address: xagorara@msu.edu.
Sci Total Environ ; 864: 161152, 2023 Mar 15.
Article in English | MEDLINE | ID: covidwho-2165831
ABSTRACT
Wastewater-based epidemiology (WBE) has drawn great attention since the Coronavirus disease 2019 (COVID-19) pandemic, not only due to its capability to circumvent the limitations of traditional clinical surveillance, but also due to its potential to forewarn fluctuations of disease incidences in communities. One critical application of WBE is to provide "early warnings" for upcoming fluctuations of disease incidences in communities which traditional clinical testing is incapable to achieve. While intricate models have been developed to determine early warnings based on wastewater surveillance data, there is an exigent need for straightforward, rapid, broadly applicable methods for health departments and partner agencies to implement. Our purpose in this study is to develop and evaluate such early-warning methods and clinical-case peak-detection methods based on WBE data to mount an informed public health response. Throughout an extended wastewater surveillance period across Detroit, MI metropolitan area (the entire study period is from September 2020 to May 2022) we designed eight early-warning methods (three real-time and five post-factum). Additionally, we designed three peak-detection methods based on clinical epidemiological data. We demonstrated the utility of these methods for providing early warnings for COVID-19 incidences, with their counterpart accuracies evaluated by hit rates. "Hit rates" were defined as the number of early warning dates (using wastewater surveillance data) that captured defined peaks (using clinical epidemiological data) divided by the total number of early warning dates. Hit rates demonstrated that the accuracy of both real-time and post-factum methods could reach 100 %. Furthermore, the results indicate that the accuracy was influenced by approaches to defining peaks of disease incidence. The proposed methods herein can assist health departments capitalizing on WBE data to assess trends and implement quick public health responses to future epidemics. Besides, this study elucidated critical factors affecting early warnings based on WBE amid the COVID-19 pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Wastewater / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Sci Total Environ Year: 2023 Document Type: Article Affiliation country: J.scitotenv.2022.161152

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Wastewater / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Sci Total Environ Year: 2023 Document Type: Article Affiliation country: J.scitotenv.2022.161152