Colloquium Talk - Geir Storvik (UiO): A sequential Monte Carlo approach to estimate the time varying reproduction number for Covid-19 compartmental models
Abstract: During the first months the covid-19 pandemic has required most countries to implement complex sequences of interventions, with the aim of controlling the transmission of the virus inthe population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces and incomplete and delayed data, inference is very challenging. We propose a state space formalisation of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with remarkable precision, on the basis of daily hospitalisation and positive test incidences. The method is in daily use in Norway and is a powerful instrument for epidemic monitoring and management. Based on joint work with Alfonso Diz-Lois Palomares, Solveig Engebretsen, Gunnar Øyvind Isaksson Rø, Kenth Engø-Monsen, Anja Bråthen Kristoffersen, Birgitte Freiesleben de Blasio and Arnoldo Frigessi (at Norwegian Institute of Public health, UiO or Norwegian Computing Center).