Wastewater-based surveillance models for COVID-19: A focused review on spatio-temporal models

From pandemic to endemic: Divergence of COVID-19 positive-tests and hospitalization numbers from SARS-CoV-2 RNA levels in wastewater of Rochester, Minnesota

“…To determine if we could use our data to predict COVID-19 case oscillations, based on SARS-CoV-2 RNA in wastewater, we applied three predictive models to the data before changes in COVID-19 case reporting guidelines (before 2022-04-25). There exist various mathematical and statistical predictive models for time series data [ [35] , [36] , [37] , [38] ]. We worked with three predictive models— autoregressive integrated moving average (ARIMA),distributed lag (DL), and autoregressive distributed lag (ADL)—for lag analysis based on two primary considerations: 1) their common usage in analogous studies [ 14 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and 2) their well-established frameworks, widespread application in time series analysis, and straightforward interpretability [ 45 ].…”

“…There exist various mathematical and statistical predictive models for time series data [ [35] , [36] , [37] , [38] ]. We worked with three predictive models— autoregressive integrated moving average (ARIMA),distributed lag (DL), and autoregressive distributed lag (ADL)—for lag analysis based on two primary considerations: 1) their common usage in analogous studies [ 14 , [38] , [39] , [40] , [41] , [42] , [43] , [44] ] and 2) their well-established frameworks, widespread application in time series analysis, and straightforward interpretability [ 45 ]. However, the models either failed to fit the data significantly or predicted lags that varied between models, indicating that our data set was not suitable for this type of predictive analysis.…”