Mathematical modeling based on RT-qPCR analysis of SARS-CoV-2 in wastewater as a tool for epidemiology

Abstract

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerges to scientific research and monitoring of wastewaters to predict the spread of the virus in the community. Our study investigated the COVID-19 disease in Bratislava, based on wastewater monitoring from September 2020 until March 2021. Samples were analyzed from two wastewater treatment plants of the city with reaching 0.6 million monitored inhabitants. Obtained results from the wastewater analysis suggest significant statistical dependence. High correlations between the number of viral particles in wastewater and the number of reported positive nasopharyngeal RT-qPCR tests of infected individuals with a time lag of 2 weeks/12 days (R2 = 83.78%/R2 = 52.65%) as well as with a reported number of death cases with a time lag of 4 weeks/27 days (R2 = 83.21%/R2 = 61.89%) was observed. The obtained results and subsequent mathematical modeling will serve in the future as an early warning system for the occurrence of a local site of infection and, at the same time, predict the load on the health system up to two weeks in advance.

A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater

“…Several works have addressed the problem of mathematical processing of wastewater data. Some of them worked on the correlation between quantification results and new cases ( Krivoňáková et al, 2021 ). Others have done extensive analysis on WBE uncertainty, including correction of quantification results with physico-chemical parameters, and have proposed solutions for automatic outlier detection ( Wade et al, 2022 ).…”

A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data

“…Wastewater collates SARS-CoV-2 particles excreted by infected individuals irrespective of clinical symptoms or presentation, which provides an opportunity to capture the viral shedding prior to symptoms and estimate the true magnitude of viral infections in communities (Bivins et al, 2020b; Hart and Halden, 2020; Peccia et al, 2020; Randazzo et al, 2020; Saguti et al, 2021; Wu et al, 2022b). Previous work has shown that SARS-CoV-2 concentrations in wastewater were much higher than expected from clinically reported cases and predicted clinical reported data for 4-10 days (Wu et al, 2020, 2022b, Peccia et al, 2020), and up to 14 days (Krivoňáková et al, 2021; Karthikeyan et al 2020). Furthermore, the fast turnaround time of wastewater and flexible sampling strategy enable WBS to provide a near real-time monitoring of the viral transmission in the sewershed.…”

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism

“…Here, the estimation of the number of absent workers due to COVID-19 infections was performed via a Poisson regression model. Other studies also have shown the trends and strong correlation of clinical data with the SARS-CoV-2 RNA abundance in wastewater on a weekly basis (Wartell et al 2022;Fahrenfeld et al 2022;Halwatura et al 2022;Krivoňáková et al 2021).…”

“…Still, as the N1-signal gave a better Adjusted R 2 coefficient, it was only included in the final prediction model. Previous studies utilizing wastewater data had focused on models to describe, estimate, and interpret SARS-CoV-2 RNA concentrations across general populations (de Sousa et al 2022;Karthikeyan et al 2021;Krivoňáková et al 2021;Vallejo et al 2022;Ahmed et al 2020;Hasan et al 2021;Saththasivam et al 2021). Simulation of infection prevalence based on the viral RNA concentration in sewage has been developed using different modeling approaches, including Monte Carlo simulations, susceptible-exposed-infectious-recovered (SEIR) model, autoregressive integrated moving average (ARIMA) models (de Sousa et al 2022;McMahan et al 2021;Karthikeyan et al 2021;Wu et al 2022).…”