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. 2023 Mar 15:864:161152.
doi: 10.1016/j.scitotenv.2022.161152. Epub 2022 Dec 23.

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

Liang Zhao  1 , Yangyang Zou  1 , Randy E David  2 , Scott Withington  2 , Stacey McFarlane  3 , Russell A Faust  4 , John Norton  5 , Irene Xagoraraki  6
Affiliations

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

Liang Zhao et al. Sci Total Environ. .

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.

Keywords: COVID-19; Early warning methods; Peak-defining methods; Public health response; SARS-CoV-2; Wastewater-based epidemiology.

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Conflict of interest statement

Declaration of competing interest All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
a. COVID-19 cases in the city of Detroit, as well as Wayne, Macomb, and Oakland counties; b. 7-day moving average of COVID-19 cases.
Fig. 2
Fig. 2
Methods of defining peaks for total COVID-19 cases. a. Method I defined peaks (gray shaded area) of total COVID-19 cases b. Method II defined peaks (gray shaded area) of total COVID-19 cases c. Method III defined peaks (gray shaded area) of total COVID-19 cases.
Fig. 3
Fig. 3
a. Major statewide, citywide, and countywide COVID-19 public health policies in the Detroit metropolitan area; b. Major public holidays in Michigan, USA; c. w/c ratio between N1 N2 gene concentrations and 7-day moving average of total COVID-19 cases.
Fig. 4
Fig. 4
Real-time early warning methods: OBM and PPCS based on N1 (gc/L): a. First early warnings of each peak identified by OBM (N1, gc/L) with Method I defined peaks b. First early warnings of each peak identified by OBM (N1, gc/L) with Method II defined peaks c. First early warnings of each peak identified by OBM (N1, gc/L) with Method III defined peaks d. Early warnings identified by PPCS (N1, gc/L) with Method I defined peaks e. Early warnings identified by PPCS (N1, gc/L) with Method II defined peaks f. Early warnings identified by PPCS (N1, gc/L) with Method III defined peaks.
Fig. 5
Fig. 5
Post-factum early warning methods MSD and PER, based on N1 (gc/L) and N1/c (gc/L/case), respectively. a. Early warnings identified by MSD (N1, gc/L) with Method I defined b. Early warnings identified by MSD (N1, gc/L) with Method II defined c. Early warnings identified by MSD (N1, gc/L) with Method III defined d. Early warnings identified by PER (N1, gc/L/case) with Method I defined peaks e. Early warnings identified by PER (N1, gc/L/case) with Method II defined peaks f. Early warnings identified by PER (N1, gc/L/case) with Method III defined peaks.
Fig. 6
Fig. 6
Hit rates of real-time early warning methods: OBM, PPC, and PPCS, with three peak-defining methods: method I, method II, and method III, based on N1 and N2 gene concentrations (gc/L).
See this image and copyright information in PMC

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