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In Environmental research ; h5-index 67.0

A pilot study was conducted from late October 2020 until mid-April 2021, aiming to examine the association between SARS-CoV-2 RNA concentrations in untreated wastewater and recorded COVID-19 cases in two Greek municipalities. A population of Random Forest and Linear Regression Machine learning models was trained and evaluated incorporating the concentrations of SARS-CoV-2 RNA in 111 wastewater samples collected from the inlets of two Wastewater Treatment Plants, along with physicochemical parameters of the wastewater influent. The model's predictions were adequately associated with the 7-day cumulative cases with the correlation coefficients (after 5-fold cross validation) ranging from 0.754 to 0.960 while the mean relative errors ranged from 30.42% to 59.46%. Our results provide indications that wastewater-based predictions can be applied in diverse settings and in prolonged time periods, although the accuracy of these predictions may be mitigated. Wastewater-based epidemiology can support and strengthen epidemiological surveillance.

Koureas Michalis, Amoutzias Grigoris D, Vontas Alexandros, Kyritsi Maria, Pinaka Ourania, Papakonstantinou Argyrios, Dadouli Katerina, Hatziinikou Marina, Koutsolioutsou Anastasia, Mouchtouri Varvara A, Speletas Matthaios, Tsiodras Sotirios, Hadjichristodoulou Christos


COVID-19, Machine learning, RNA, RT-PCR, SARS-CoV-2, Wastewater-based epidemiology (WBE)