University of Cambridge > > Isaac Newton Institute Seminar Series > PLENARY TALK - Spatial modelling of early-phase COVID-19 epidemic in Norway

PLENARY TALK - Spatial modelling of early-phase COVID-19 epidemic in Norway

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IDP - Infectious Dynamics of Pandemics: Mathematical and statistical challenges in understanding the dynamics of infectious disease pandemics

We developed a stochastic SEIR model for the COVID -19 epidemic at a fine spatial scale, using mobile phone mobility data, to describe the geographical spread of the virus. The model is developed for the very early phase of the epidemic, when movement of individuals plays a key role. In the beginning, importation of the virus into the region of interest is decisive, and we estimate the proportion of unknown imported cases. Our model represents non-pharmaceutical interventions to contain the epidemic by means of a regionally varying step function of the effective reproduction numbers. The regionally varying effective reproduction numbers are estimated by sequential Approximate Bayesian Computing, using hospitalisation data of the infected individuals at regional level. For prediction, we develop a way to regularise the mobility matrices, to conserve the geographical distribution of the population. This allows adequate long term predictions of all quantities of interest. Uncertainty in the parameters (both the estimated ones and the ones learned from the literature) is prolonged into the future by simulation. We use our model to describe the history of the first phase of the COVID -19 epidemic in Norway, during which social distancing and hygienic measures have been adopted together with teleworking and school closure and reopening. The result of these measures have reduced the presence of the virus in Norway to such a low level, leading to a relaxation of restrictions, to resemble the early phase of the epidemic, making our model again important. We compare the results of our model to the ones obtained by a similar nonregional model. We also developed a version of the model which has a time varying reproduction number. In this case we resort to Sequential Monte Carlo for inference. I will discuss the difficulties in making predictions using this model.  This is joint work with  Birgitte Freiesleben de Blasio, Solveig Engebretsen, Gunnar Isaksson Rø, Alfonso Diz-Lois Palomares, Kenth Engø-Monsen, Anja Bråthen Kristoffersen and Geir Storvik.

This talk is part of the Isaac Newton Institute Seminar Series series.

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