Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology

Abstract

Wastewater-based epidemiology (WBE) has been regarded as a potential tool for the prevalence estimation of coronavirus disease 2019 (COVID-19) in the community. However, the application of the conventional back-estimation approach is currently limited due to the methodological challenges and various uncertainties. This study systematically performed meta-analysis for WBE datasets and investigated the use of data-driven models for the COVID-19 community prevalence in lieu of the conventional WBE back-estimation approach. Three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) were applied to the multi-national WBE dataset. To evaluate the robustness of these models, predictions for sixteen scenarios with partial inputs were compared against the actual prevalence reports from clinical testing. The performance of models was further validated using unseen data (data sets not included for establishing the model) from different stages of the COVID-19 outbreak. Generally, ANN and ANFIS models showed better accuracy and robustness over MLR models. Air and wastewater temperature played a critical role in the prevalence estimation by data-driven models, especially MLR models. With unseen datasets, ANN model reasonably estimated the prevalence of COVID-19 (cumulative cases) at the initial phase and forecasted the upcoming new cases in 2-4 days at the post-peak phase of the COVID-19 outbreak. This study provided essential information about the feasibility and accuracy of data-driven estimation of COVID-19 prevalence through the WBE approach.

SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation

“…Many research groups all over the world are monitoring wastewater for SARS-CoV-2 RNA. However, potential impacts of temporal and spatial variables on the correlation of RNA and human infection prevalence have been posed ( Bivins et al, 2020 ; Li et al, 2021b ). It is also important to investigate these relationships in different contexts, such as in low-income areas and rural locations (with decentralized infrastructure for wastewater management) and/or in urban centralized wastewater facilities.…”

“…Possible recommendations may be towards lockdown or quarantine. Researchers in Australia, the Netherlands, Sweden, and the USA have already detected SARS-CoV-2 in wastewater ( Li et al, 2021a , b ; Malapatty, 2020 ; Medema et al, 2020a , b ). During the early week of the outbreak of COVID-19 in New Haven (Connecticut) several cases were tracked and declared positive by confirming the presence of SARS-CoV-2 RNA in primary sewage sludge from local wastewater treatment plants ( Peccia et al, 2020 ).…”

SARS-CoV-2 adsorption on suspended solids along a sewerage network: mathematical model formulation, sensitivity analysis, and parametric study

“…Such virus signals have been found to indicate global community infections but, apparently, they are unable to identify local spots of rising infections within the community. Even for global estimations, the situation gets complicated by the survival and partitioning (between liquid and solid phase) properties of viruses in the aqueous environments of sewage which can mask the actual virus profile and so mislead their quantification (Corpuz et al 2020 ; Li et al 2021a , b ).…”

The Rapid Assessment of Aggregated Wastewater Samples for Genomic Surveillance of SARS-CoV-2 on a City-Wide Scale

“…These efforts were initially designed to monitor the trends of "true" incidence of SARS-CoV-2 infection within the targeted communities, as wastewater sampling represents a non-invasive means of quantitatively assessing the presence of SARS-CoV-2 nucleic acid derived from individuals participating and non-participating in clinical testing efforts [1,[7][8][9][10][11][12]. Thus, the sampling of wastewater represents a means by which SARS-CoV-2 public health may be assessed on a large scale to determine the disease / infection burden [2,8,9,[13][14][15][16][17].…”

“…The genetic diversity of SARS-CoV-2 has largely been determined via the sequencing of clinical samples obtained from infected individuals; however, changes in the viral genome have been shown to be detectable in wastewater samples [18][19][20][21][22][23]. Indeed, the presence of specific SARS-CoV-2 variants is often detectable in the wastewater of a community prior to detection in clinical samples [2,5,9,13,21,23,24]. As such, the genomic surveillance of SARS-CoV-2 through wastewater sampling represents a cost-effective and unbiased means by which the community prevalence and genetic diversity of SARS-CoV-2 infections may be assessed.…”