# name
Analysis of SARS-CoV-2 in wastewater for prevalence estimation and investigating clinical diagnostic test biases

# metadata.dc.description.abstract[].value
Here we analyze SARS-CoV-2 genome copies in Catalonia's wastewater during the Omicron peak and develop a mathematical model to estimate the number of infections and the temporal relationship between reported and unreported cases. 1-liter samples from 16 wastewater treatment plants were collected and used in a compartmental epidemiological model. The average correlation between genome copies and reported cases was 0.85, with an average delay of 8.8 days. The model estimated that 53% of the population was infected, compared to the 19% reported cases. The under-reporting was highest in November and December 2021. The maximum genome copies shed in feces by an infected individual was estimated to range from 1.4×108 gc/g to 4.4×108 gc/g. Our framework demonstrates the potential of wastewater data as a leading indicator for daily new infections, particularly in contexts with low detection rates. It also serves as a complementary tool for prevalence estimation and offers a general approach for integrating wastewater data into compartmental models.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Mattei, Mattia et al., 2023

# metadata.dc.source[].value
Articles publicats en revistes (Genètica, Microbiologia i Estadística)

# metadata.dc.title[].value
Analysis of SARS-CoV-2 in wastewater for prevalence estimation and investigating clinical diagnostic test biases