  Wastewater based epidemiology is recognized as one of the monitoring pillars,
providing essential information for pandemic management. Central in the
methodology are data modelling concepts for both communicating the monitoring
results but also for analysis of the signal. It is due to the fast development
of the field that a range of modelling concepts are used but without a coherent
framework. This paper provides for such a framework, focusing on robust and
simple concepts readily applicable, rather than applying latest findings from
e.g., machine learning. It is demonstrated that data preprocessing, most
important normalization by means of biomarkers and equal temporal spacing of
the scattered data, is crucial. In terms of the latter, downsampling to a
weekly spaced series is sufficient. Also, data smoothing turned out to be
essential, not only for communication of the signal dynamics but likewise for
regressions, nowcasting and forecasting. Correlation of the signal with
epidemic indicators require multivariate regression as the signal alone cannot
explain the dynamics but simple linear regression proofed to be a suitable tool
for compensation. It was also demonstrated that short term prediction (7 days)
is accurate with simple models (exponential smoothing or autoregressive models)
but forecast accuracy deteriorates fast for longer periods.
