Modelling Wastewater Data from Austria Using Generalized Additive Models for Location, Scale and Shape (GAMLSS)
Abstract
Generalized Additive Models for Location, Scale and Shape (GAMLSS) are a flexible alternative to standard regression models, where only the mean of the response variable is modelled in terms of covariates. However, GAMLSS currently are seldom used in statistical applications. In this paper, we analyze data from wastewater measurements in Austria with respect to COVID-19. The goal is to model the viral load in the wastewater in terms of covariates. The results show that both the vaccination rate and the dominant virus variant are important covariates in the analysis of COVID-19 related viral load in the wastewater. Moreover, complex GAMLSS based on the four-parametric Box-Cox t distribution clearly outperformed simpler Generalized Additive Models (GAMs) based on the Gamma distribution. Moreover, GAMLSS show also better predictive performance than GAMs.
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