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

Hiroki Ando; Michio Murakami; Warish Ahmed; Ryo Iwamoto; Satoshi Okabe; Masaaki Kitajima

doi:10.1016/j.envint.2023.107743

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

Environ Int. 2023 Mar:173:107743. doi: 10.1016/j.envint.2023.107743. Epub 2023 Jan 7.

Authors

Hiroki Ando¹, Michio Murakami², Warish Ahmed³, Ryo Iwamoto⁴, Satoshi Okabe¹, Masaaki Kitajima⁵

Affiliations

¹ Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.
² Center for Infectious Disease Education and Research, Osaka University, 2-8 Yamadaoka, Suita, Osaka 565-0871, Japan.
³ CSIRO Environment, Ecosciences Precinct, 41 Boggo Road, QLD 4102, Australia.
⁴ Shionogi & Co. Ltd, 1-8, Doshomachi 3-Chome, Chuo-ku, Osaka, Osaka 541-0045, Japan; AdvanSentinel Inc, 1-8 Doshomachi 3-Chome, Chuo-ku, Osaka, Osaka 541-0045, Japan.
⁵ Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan. Electronic address: mkitajima@eng.hokudai.ac.

Abstract

Wastewater-based epidemiology (WBE) has the potential to predict COVID-19 cases; however, reliable methods for tracking SARS-CoV-2 RNA concentrations (C_RNA) 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 C_RNA 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 C_RNA 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.

Keywords: COVID-19; EPISENS-M; Mathematical model; Quantification method; SARS-CoV-2; Wastewater-based epidemiology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19* / diagnosis
Humans
Models, Theoretical
RNA, Viral*
SARS-CoV-2 / genetics
Wastewater

Substances

RNA, Viral
Wastewater