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

Abstract

Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 – Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds (R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.

Predictive power of wastewater for nowcasting infectious disease transmission: a retrospective case study of five sewershed areas in Louisville, Kentucky

“…25 Despite potential bias introduced by using the wastewater data to inform the parameterization of those data in the model, the additive and substitutive power in this study were not very strong, when compared with a preprint study in nowcasting SARS-CoV-2 infections in the Boston metropolitan area, covering a much larger population. 13 This further supports the need for future research into the conditions required for using wastewater data in infectious disease nowcasting. Finally, our models included a simple linear relationship between wastewater data and modeled infections.…”

“…[9][10][11] Wastewater data has been used in nowcasts of large metropolitan areas, and it has been found that there is no definitive agreement on a case count to water concentration. 8,[12][13][14] Nevertheless, WBE for surveillance has its own unique challenges, including variation in sampling and quantification methods, and challenges mapping wastewater levels to estimates of local disease burden. [15][16][17] As a result, the value of wastewater data for nowcasting disease transmission in subpopulations in addition to, or instead of, traditional surveillance data remains unclear.…”

Wastewater-based reproduction numbers and projections of COVID-19 cases in three areas in Japan, November 2021 to December 2022

“…Mechanistic models have been applied to wastewater data in recent studies, with the primary aim of evaluating the predictive ability of models [ 18 , 19 ] or monitoring growth trends by computing effective reproduction numbers [ 20 , 21 ]. Yet, another important component, scenario modelling, has not been thoroughly explored in combination with wastewater surveillance.…”

“…To this end, we need to exploit wastewater data and incorporate current transmission mechanisms, e.g. repeated infections related to emerging variants and waning immunity, which have not been explicitly captured in previous work [ 18 , 19 ].…”

Wastewater-based reproduction numbers and projections of COVID-19 cases in multiple cities in Japan, 2022

“…Mechanistic models have been applied to wastewater data in recent studies, with the primary aim of evaluating the predictive ability of models [18,19] or monitoring growth trends by computing effective reproduction numbers [20,21]. Yet, another important component, scenario modelling, has not been thoroughly explored in the combination of wastewater surveillance.…”