Wastewater-based prediction of COVID-19 cases using a highly sensitive SARS-CoV-2 RNA detection method combined with mathematical modeling

• Published in: Environment international Vol. 173; p. 107743
• Main Authors: Ando, Hiroki, Murakami, Michio, Ahmed, Warish, Iwamoto, Ryo, Okabe, Satoshi, Kitajima, Masaaki
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.03.2023; Published by Elsevier Ltd; Elsevier
• Subjects: COVID-19; COVID-19 - diagnosis; EPISENS-M; Full Length; Humans; Mathematical model; Models, Theoretical; Quantification method; RNA, Viral; SARS-CoV-2; SARS-CoV-2 - genetics; Wastewater; Wastewater-based epidemiology; COVID-19; SARS-CoV-2; Mathematical model; Wastewater-based epidemiology; Quantification method; EPISENS-M
• ISSN: 0160-4120; 1873-6750; 1873-6750
• DOI: 10.1016/j.envint.2023.107743

[Display omitted] •EPISENS-M consists of RNA extraction from membrane, RT-preamplification, and qPCR.•EPISENS-M enables sensitive detection of SARS-CoV-2 RNA from wastewater.•SARS-CoV-2 RNA concentrations in wastewater correlated well with the reported cases.•A mathematical model was developed for predicting reported COVID-19 cases via WBE.•EPISENS-M with the mathematical model enables reliable prediction of reported cases. Wastewater-based epidemiology (WBE) has the potential to predict COVID-19 cases; however, reliable methods for tracking SARS-CoV-2 RNA concentrations (CRNA) in wastewater are lacking. In the present study, we developed a highly sensitive method (EPISENS-M) employing adsorption-extraction, followed by one-step RT-Preamp and qPCR. The EPISENS-M allowed SARS-CoV-2 RNA detection from wastewater at 50 % detection rate when newly reported COVID-19 cases exceed 0.69/100,000 inhabitants in a sewer catchment. Using the EPISENS-M, a longitudinal WBE study was conducted between 28 May 2020 and 16 June 2022 in Sapporo City, Japan, revealing a strong correlation (Pearson’s r = 0.94) between CRNA and the newly COVID-19 cases reported by intensive clinical surveillance. Based on this dataset, a mathematical model was developed based on viral shedding dynamics to estimate the newly reported cases using CRNA data and recent clinical data prior to sampling day. This developed model succeeded in predicting the cumulative number of newly reported cases after 5 days of sampling day within a factor of √2 and 2 with a precision of 36 % (16/44) and 64 % (28/44), respectively. By applying this model framework, another estimation mode was developed without the recent clinical data, which successfully predicted the number of COVID-19 cases for the succeeding 5 days within a factor of √2 and 2 with a precision of 39 % (17/44) and 66 % (29/44), respectively. These results demonstrated that the EPISENS-M method combined with the mathematical model can be a powerful tool for predicting COVID-19 cases, especially in the absence of intensive clinical surveillance.