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

• Published in: The Science of the total environment Vol. 858; no. Pt 1; p. 159680
• Main Authors: Daza-Torres, Maria 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, Bischel, Heather N.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.02.2023; The Author(s). Published by Elsevier B.V
• Subjects: Bayes Theorem; Bayesian inference; COVID-19 - epidemiology; COVID–19; Humans; Incidence; Public health surveillance: sewage; SARS-CoV-2; Statistical models; Viral Load; Wastewater; Wastewater-based epidemiology (WBE); COVID–19; SARS-CoV-2; Wastewater-based epidemiology (WBE); Public health surveillance: sewage; Statistical models; Bayesian inference
• ISSN: 0048-9697; 1879-1026; 1879-1026
• DOI: 10.1016/j.scitotenv.2022.159680

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence. [Display omitted] •We introduce two models to relate SARS-CoV-2 wastewater data to clinical case data.•Adequate training periods are essential for accurate projection of cases.•A testing period is classified as adequate when test rates exceed case rates.•Similar case projections were obtained using the two models.•The effective reproductive number was estimated using case predictions.