Webinar: Safety & Security research in times of Covid-19: what can we learn from our own TU Delft experts?

Nieuws - 01 oktober 2020 - Webredactie

‘Week van de Veiligheid’ – TU Delft Safety & Security Institute
Tuesday October 6, 12.00 – 13.00
You can send in your questions! @Eveline e.m.vreede@tudelft.nl

Life after Lockdown: How TU Delft engages with the Dutch population in Covidexit research – dr. Anatol Itten
The effectiveness of lifting corona measures strongly depends on the behaviour of Dutch citizens. This in turn depends on the extent to which the Dutch consider the measures taken by the government to be necessary, fair and compatible with their everyday lives. Researchers of TBM at TU Delft and the RIVM joined forces to find out how the Dutch population felt about lifting corona measures in a so-called participatory value evaluation (PVE). They created a novel online participation tool which was completed by over 30,000 citizens. In this presentation, they will explain how a PVE works and how citizens were willing to think along with the government. As there are still many lockdowns around the world, restricted traveling and uncertainty about vaccination, the researchers showcase that integrating the public in policy dilemmas is crucial in understanding what works best for as many people as possible in their fight against COVID-19.

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Virus spreading in public transport networks - Panchamy Krishnakumari
Public transport worldwide is heavily affected by the corona virus pandemic. In a period where sharing and crowding are becoming a public health concern, the connectivity and efficiency offered by mass transport become a potential peril. The presentation will address how we can leverage existing data sources to answer some of the current issues. How quickly and adversely does a virus spread in a public transport system? How many will potentially get infected if pre-pandemic ridership levels are expected while aiming to maintain social distancing? We explore this by studying the evolution of contract networks and an epidemiological model using smart card data from the Washington DC metro network.

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