Shocks and Resilience

Measuring policy impact in the COVID-19 crisis and building resilience against future shocks.

By The Alan Turing Institute - London, UK in Urban Analytics Spatial Modelling Data fusion

April 1, 2021

Project description

The COVID-19 crisis has highlighted how vulnerable societies and governments are to shocks. This vulnerability is exacerbated by the propensity to design policy for narrow silos relating to singular policy areas and government departments, without adequate consideration of the interdependencies between them and the interconnected nature of local and global societies. The pandemic has brought into focus the fact that resilience in one policy area (e.g. health) can come at the cost of resilience in another (e.g. the economy). The overall aim of this large-scale, 2-year research project is to develop a better understanding of resilience in interconnected health, social, and economic systems and to use this understanding to identify robust policy measures. To get more information about this project visit this link:

My role

I work within a multidisciplinary team that include experts in several areas such as health, public policy, economics, and urban analytics. I support the work package 3, related to spatial modelling which aim to develop spatial modelling methods or tools that can be integrated within the epidemiologic-socio-economic models to tackle policy questions that are relevant at the sub-national level (e.g., regional and local authorities).

We developed an innovated tool called The Synthetic Population Catalyst - SPC, that integrate a vary of data sources in great Britain to outcome a single file with the social-economic characteristics and interaction of the individuals of a given area. We hope that methods and tools we produce offer better means for ‘what if’ scenario modelling in relation to spatial variations in policy regulations, such as the local relaxation or reintroduction of social distancing rules, or local controls over business, leisure, and education.

Posted on:
April 1, 2021
2 minute read, 275 words
Urban Analytics Spatial Modelling Data fusion
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