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

We simulate different, hypothetical interventions following the initial phase in order to show that both, the amount of change in behavior (leading to a change in spreading rate λ, Fig. 2 A,B ) and the exact timing of the change (Fig. 2 C,D) determine the future development. Hypothetical interventions build on the inferred parameters from the initial phase ( Fig. 1 , in particular median λ 0 = 0.41 and median µ = 0.12) and were implemented as change points in the spreading rate from the inferred λ 0 to a new value λ 1 . With such a change point, we model three potential scenarios of public behavior: (I) No social distancing; Public behavior is unaltered and the spread continues with the inferred rate (λ 1 = λ 0 with median λ 1 = 0.41 > µ). (II) Mild social distancing; The spreading rate decreases to 50%, (λ 1 = λ 0 / 2 with median λ 1 = 0.21 > µ). Although people effectively reduce the number of contacts by a factor of two in this second scenario, the total number of reported cases continues to grow alongside scenario (I) for the time period of the reporting delay D (median D = 8.6 from initial phase, see below for a more constrained estimation). All rights reserved. No reuse allowed without permission.