BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:PLENARY TALK - Spatial modelling of early-phase COVID-19 epidemic in Norway - Arnoldo Frigessi (University of Oslo) DTSTART:20200724T110000Z DTEND:20200724T120000Z UID:TALK150100@talks.cam.ac.uk CONTACT:INI IT DESCRIPTION:We developed a stochastic SEIR model for the COVID-19
epide mic at a fine spatial scale\, using mobile phone mobility data\, to descri be
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 im ported 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 re production numbers are estimated by sequential
Approximate Bayesian Com puting\, using hospitalisation data of the infected
individuals at regi onal level. For prediction\, we develop a way to regularise
the mobilit y matrices\, to conserve the geographical distribution of the
populatio n. This allows adequate long term predictions of all quantities of
inte rest. Uncertainty in the parameters (both the estimated ones and the ones< br>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 CO VID-19 epidemic
in Norway\, during which social distancing and hygienic measures have been
adopted together with teleworking and school closur e 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 o f restrictions\, to resemble the early phase
of the epidemic\, making o ur model again important. We compare the results of
our model to the on es obtained by a similar nonregional model. We also
developed a version of the model which has a time varying reproduction number.
In this cas e we resort to Sequential Monte Carlo for inference. I will discuss
the difficulties in making predictions using this model. This is joint work w ith Birgitte Freiesleben de Blasio\, Solveig
Engebretsen\, Gunnar Isaks son Rø\;\, Alfonso Diz-Lois Palomares\, Kenth Engø\;-Monsen\,< br>Anja Brå\;then Kristoffersen and Geir Storvik. LOCATION:Seminar Room 1\, Newton Institute END:VEVENT END:VCALENDAR