The impact of signal variability on COVID-19 epidemic growth rate estimation from wastewater surveillance data.

ABSTRACT: Testing samples of wastewater for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Here we introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gen

SUBMITTER: Colman E 

PROVIDER: S-EPMC12118983 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature