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

During the initial outbreak of an epidemic, reliable short-term forecasts are key to estimate required medical capacities, and to inform and advice the public and decision makers [1] . During this initial phase, three tasks are of particular importance to provide time-critical information for crisis mitigation: (1) establishing central epidemiological parameters such as the basic reproduction number that can be used for short-term forecasting; (2) simulating the effects of different possible interventions aimed at the mitigation of the outbreak; (3) estimating the actual effects of the measures taken -to rapidly adjust them and to adapt short-term forecasts. Tackling these tasks is challenging due to the large statistical and systematic errors that are present during the initial stages of an epidemic with its low case numbers. This is further complicated by the fact that mitigation measures are taken rapidly, while the outbreak unfolds, but they take an effect only after an a priori unknown delay. To obtain reasonable parameter estimates for short-term forecasting and policy evaluation despite these complications, any prior knowledge available needs to be integrated into modeling efforts to reduce uncertainties. This includes knowledge about basic mechanisms of disease transmission, recovery, as well as preliminary estimates of epidemiological parameters from other countries, or from closely related pathogens. The integration of prior knowledge, the quantitative assessment of the remaining uncertainties about epidemiological parameters, and the principled propagation of these uncertainties into forecasts is the domain of Bayesian modeling and inference [2, 3] .