Inferring COVID-19 spreading rates and potential change points for case number forecasts

Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann

Here, we draw on an established class of models for epidemic outbreaks: The Susceptible-Infected-Recovered (SIR) model [4] [5] [6] [7] specifies the rates with which population compartments change over time, i.e., with which susceptible people become infectious, or infectious people recover. This simple model can be formulated in terms of coupled ordinary differential equations (in mean field), which enable analytical treatment [8, 9] or fast evaluation (ideally suited for Bayesian inference). Accordingly, SIR-like models have been used to model epidemic spreads, from Bayesian Markov-Chain Monte Carlo (MCMC) parameter estimation [10] [11] [12] to detailed scenario discussions [13] [14] [15] [16] . Recently, this family of models also played a dominant role in the analyses of the global corona virus (SARS-CoV-2) outbreak, from inference [17] [18] [19] to scenario forecast [20] [21] [22] [23] [24] [25] [26] [27] to control strategies [28, 29] .