@article{3030244,
    title = "Wastewater monitoring as a supplementary surveillance tool for capturing
SARS-COV-2 community spread. A case study in two Greek municipalities",
    author = "Koureas, Michalis and Amoutzias, Grigoris D. and Vontas, Alexandros and and Kyritsi, Maria and Pinaka, Ourania and Papakonstantinou, Argyrios and and Dadouli, Katerina and Hatzinikou, Marina and Koutsolioutsou, Anastasia and and Mouchtouri, Varvara A. and Speletas, Matthaios and Tsiodras, and Sotirios and Hadjichristodoulou, Christos",
    journal = "Marine Environmental Research",
    year = "2021",
    volume = "200",
    publisher = "ACADEMIC PRESS INC ELSEVIER SCIENCE",
    issn = "0141-1136",
    doi = "10.1016/j.envres.2021.111749",
    keywords = "Wastewater-based epidemiology (WBE); COVID-19; SARS-CoV-2; Machine
learning; RNA; RT-PCR",
    abstract = "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."
}