Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology. (30th June 2022)

• Record Type: Journal Article
• Authors: Jiang, Guangming; Wu, Jiangping; Weidhaas, Jennifer; Li, Xuan; Chen, Yan; Mueller, Jochen; Li, Jiaying; Kumar, Manish; Zhou, Xu; Arora, Sudipti; Haramoto, Eiji; Sherchan, Samendra; Orive, Gorka; Lertxundi, Unax; Honda, Ryo; Kitajima, Masaaki; Jackson, Greg
• Is Part Of: Water research. Volume 218(2022)
• Publication Date: 2022-06-30
• Subjects: COVID-19 -- Wastewater-based epidemiology -- SARS-CoV-2 -- Artificial neural network -- Prevalence -- Incidence; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2022.118451
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: ANN models to estimation COVID case numbers developed using >10, 000 WBE data. The model inputs include data of WBE, weather, clinical testing and vaccine coverage. The ANN model accurately estimates COVID incidence and prevalence rate. ANN model can also estimate the COVID effective reproduction rate effectively. The lead-time of early warning of incidence was determined to be 2-4 days. Abstract: As a cost-effective and objective population-wide surveillance tool, wastewater-based epidemiology (WBE) has been widely implemented worldwide to monitor the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater. However, viral concentrations or loads in wastewater often correlate poorly with clinical case numbers. To date, there is no reliable method to back-estimate the coronavirus disease 2019 (COVID-19) case numbers from SARS-CoV-2 concentrations in wastewater. This greatly limits WBE in achieving its full potential in monitoring the unfolding pandemic. The exponentially growing SARS-CoV-2 WBE dataset, on the other hand, offers an opportunity to develop data-driven models for the estimation of COVID-19 case numbers (both incidence and prevalence) and transmission dynamics (effective reproduction rate). This study developed artificial neural network (ANN) models by innovatively expanding a conventional WBE dataset to include catchment, weather, clinical testing coverage and vaccination rate. The ANN models were trained and evaluated with a comprehensive state-wide wastewater monitoring dataset from Utah, USA during May 2020 to December 2021. In diverse sewer catchments, ANN models were found to accurately estimate the COVID-19 prevalence and incidence rates, with excellent precision for prevalence rates. Also, an ANN model was developed to estimate the effective reproduction number from both wastewater data and other pertinent factors affecting viral transmission and pandemic dynamics. The established ANN model was successfully validated for its transferability to other states or countries using the WBE dataset from Wisconsin, USA. Graphical Abstract: Image, graphical abstract