Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology.
Xuan Li, Jagadeeshkumar Kulandaivelu, Shuxin Zhang, Jiahua Shi, Muttucumaru Sivakumar, Jochen Mueller, Stephen Luby, Warish Ahmed, Lachlan Coin, Guangming Jiang
Sci Total Environ | Elsevier BV | Published : 2021
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 ..View full abstract