Five-week warning of COVID-19 peaks prior to the Omicron surge in Detroit, Michigan using wastewater surveillance

Wastewater-based epidemiology (WBE) is useful in predicting temporal fluctuations of COVID-19 incidence in com-munities and providing early warnings of pending outbreaks. To investigate the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in communities, a 12-month study between September 1, 2020, and August 31, 2021, prior to the Omicron surge, was conducted. 407 untreated wastewater samples were col-lected from the Great Lakes Water Authority (GLWA) in southeastern Michigan. N1 and N2 genes of SARS-CoV-2 were quantified using RT-ddPCR. Daily confirmed COVID-19 cases for the City of Detroit, and Wayne, Macomb, Oakland counties between September 1, 2020, and October 4, 2021, were collected from a public data source. The total concen-trations of N1 and N2 genes ranged from 714.85 to 7145.98 gc/L and 820.47 to 6219.05 gc/L, respectively, which were strongly correlated with the 7-day moving average of total daily COVID-19 cases in the associated areas, after 5 weeks of the viral measurement. The results indicate a potential 5-week lag time of wastewater surveillance preced-ing COVID-19 incidence for the Detroit metropolitan area. Four statistical models were established to analyze the re-lationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in the study areas. Under a 5 -week lag time scenario with both N1 and N2 genes, the autoregression model with seasonal patterns and vector autoregression model were more effective in predicting COVID-19 cases during the study period. To investigate the impact of flow parameters on the correlation, the original N1 and N2 gene concentrations were normalized by waste-water flow parameters. The statistical results indicated the optimum models were consistent for both normalized and non-normalized data. In addition, we discussed parameters that explain the observed lag time. Furthermore, we eval-uated the impact of the omicron surge that followed, and the impact of different sampling methods on the estimation of lag time

Automated summary

Wastewater-based epidemiology (WBE) is a useful tool for predicting COVID-19 incidence and providing early warnings. In this study, the relationship between SARS-CoV-2 concentrations in wastewater and COVID-19 incidence in southeastern Michigan was examined. The results showed a strong correlation between the viral concentrations in wastewater and the daily COVID-19 cases, with a potential 5-week lag time. Statistical models were established to predict COVID-19 cases, and the autoregression model with seasonal patterns and vector autoregression model were found to be more effective. Flow parameters had little impact on the correlation, and the optimum models worked well for both normalized and non-normalized data. The study also discussed the factors contributing to the observed lag time and evaluated the impact of the Omicron variant and different sampling methods.