Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Daza–Torres, María L.; Montesinos-López, J. Cricelio; Kim, Minji; Olson, Rachel; Bess, C. Winston; Rueda, Lezlie; Susa, Mirjana; Tucker, Linnea; García, Yury E.; Schmidt, Alec J.; Naughton, Colleen C.; Pollock, Brad H.; Shapiro, Karen; Nuño, Miriam A; Bischel, Heather N.

doi:10.1016/j.scitotenv.2022.159680

Cited by 14 publications

(20 citation statements)

References 53 publications

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“…Critically, differences in the slopes of the case and wastewater curves may also have affected the ratio between them. As has been shown in previous studies [23], we suggest that proportionally more cases remained undetected at the very beginning of a surge until the diagnostic testing rates adapted, as the case curves increased more steeply than the wastewater curves before the peak of each surge ( Figure S5 ). This affected the ratio as well ( Table 2 ).…”

Section: Resultssupporting

confidence: 81%

“…Additional work could also incorporate hospitalization [7], vaccination, mobility and other data types that were not considered here. Importantly, modeling work should ensure that the case data used to train a predictive model are drawn from a period(s) when testing was adequate [23]. Additionally, we found that within our dataset, wastewater data varied in quality, and our analysis was limited by the changing frequency of wastewater sample collection throughout each time series.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies recommend applying a 7-day or 10-day moving average to the wastewater data [23]. However, as the sampling frequencies in our dataset varied over time and space, lowess smoothing and interpolation was chosen for analysis of lag and lead times and for data visualization [3].…”

Section: Methodsmentioning

confidence: 99%

“…Modeling testing bias was shown to improve case data accuracy when compared to seroprevalence [22], but normalization in wastewater testing studies is typically focused only on accounting for wastewater strength. Although the importance of assessing testing rates prior to modeling was demonstrated in a recent study [23], to our knowledge, few wastewater studies have directly addressed bias in diagnostic testing data.…”

Section: Causes Of Inaccuracies In Diagnostic Testing Datamentioning

confidence: 99%

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The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: considerations for model training data sets

Schill

Nelson

Harris-Lovett

et al. 2022

Preprint

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During the COVID-19 pandemic, wastewater-based surveillance has been used alongside diagnostic testing to monitor infection rates. With the decline in cases reported to public health departments due to at-home testing, wastewater data may serve as the primary input for epidemiological models, but training these models is not straightforward. We explored factors affecting noise and bias in the ratio between wastewater and case data collected in 26 sewersheds in California from October 2020 to March 2022. The strength of the relationship between wastewater and case data appeared dependent on sampling frequency and population size, but was not increased by wastewater normalization to flow rate or case count normalization to testing rates. Additionally, the lead and lag times between wastewater and case data varied over time and space, and the ratio of log-transformed individual cases to wastewater concentrations changed over time. This ratio increased sequentially in the Epsilon/Alpha, Delta, and Omicron BA.1 variant surges of COVID-19 and was also related to the diagnostic testing rate. Based on this analysis, we present a framework of scenarios describing the dynamics of the case to wastewater ratio to aid in data handling decisions for ongoing modeling efforts.

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Section: Resultssupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Causes Of Inaccuracies In Diagnostic Testing Datamentioning

confidence: 99%

See 2 more Smart Citations

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: considerations for model training data sets

Schill

Nelson

Harris-Lovett

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…WBE methods have been used to detect changes in drug consumption (Zuccato et al, 2005), dietary patterns (Choi et al, 2019), and the circulation of pathogens like poliovirus and norovirus (Asghar et al, 2014). SARS-CoV-2 RNA in WW correlates well with reported cases of COVID-19 (Ai et al, 2021;Daza-Torres et al, 2023;Huisman et al, 2022). However, some studies have shown that the relationship between WW and COVID-19 clinical cases varies over time (D'Aoust et al, 2022;.…”

Section: Introductionmentioning

confidence: 99%

Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater

Montesinos-López

Daza–Torres

García

et al. 2023

Preprint

Self Cite

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Trends in COVID-19 infection have changed throughout the pandemic due to myriad factors, including changes in transmission driven by social behavior, vaccine development and uptake, mutations in the virus genome, and public health policies. Mass testing was an essential control measure for curtailing the burden of COVID-19 and monitoring the magnitude of the pandemic during its multiple phases. However, as the pandemic progressed, new preventive and surveillance mechanisms emerged. Implementing vaccine programs, wastewater (WW) surveillance, and at-home COVID-19 tests reduced the demand for mass severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. This paper proposes a sequential Bayesian approach to estimate the COVID-19 positivity rate (PR) using SARS-CoV-2 RNA concentrations measured in WW through an adaptive scheme incorporating changes in virus dynamics. PR estimates are used to compute thresholds for WW data using the CDC thresholds for low, substantial, and high transmission. The effective reproductive number estimates are calculated using PR estimates from the WW data. This approach provides insights into the dynamics of the virus evolution and an analytical framework that combines different data sources to continue monitoring the COVID-19 trends. These results can provide public health guidance to reduce the burden of future outbreaks as new variants continue to emerge. The proposed modeling framework was applied to the City of Davis and the campus of the University of California Davis.

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Mathematical Modeling and Wastewater-Based Epidemiology

Montesinos-López,

Daza–Torres,

García

et al. 2023

Handbook of Visual, Experimental and Computational Mathematics

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Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Cited by 14 publications

References 53 publications

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: considerations for model training data sets

The dynamic relationship between COVID-19 cases and SARS-CoV-2 wastewater concentrations across time and space: considerations for model training data sets

Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater

Mathematical Modeling and Wastewater-Based Epidemiology

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