Model-based and model-free characterization of epidemic outbreaks

Dehning, J.; Spitzner, F. P.; Linden, M. C.; Mohr, S. B.; Pinheiro Neto, J.; Zierenberg, J.; Wibral, M.; Wilczek, M.; Priesemann, V.

Here we provide detailed background information for our work on Bayesian inference of change-points in the spread of SARS-CoV-2 and the effectiveness of non-pharmaceutical interventions (Dehning et al., Science, 2020). We outline the general background of Bayesian inference and of SIR-like models. We explain the assumptions that underlie model-based estimates of the reproduction number and compare them to the assumptions that underlie model-free estimates, such as used in the Robert-Koch Institute situation reports. We highlight effects that originate from the two estimation approaches, and how they may cause differences in the inferred reproduction number. Furthermore, we explore the challenges that originate from data availability - such as publication delays and inconsistent testing - and explain their impact on the time-course of inferred case numbers. Along with alternative data sources, this allowed us to cross-check and verify our previous results.