Journal article
Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology
X Li, J Kulandaivelu, S Zhang, J Shi, M Sivakumar, J Mueller, S Luby, W Ahmed, L Coin, G Jiang
Science of the Total Environment | Published : 2021
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 ..
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Awarded by University of Wollongong
Funding Acknowledgements
This research was supported by the Australian Research Council Discovery Project (DP190100385) . Shuxin Zhang receives the sup-port from a University of Wollongong PhD scholarship. Guangming Jiang was a recipient of the Australian Research Council DECRA Fellowship (DE170100694) .