COVID-19: Causal analysis of confinement policies

MLG-ULB participation to the CodeVsCovid19 hackaton

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Identify the impact and effectiveness of government policy on the number of infected and deaths.


We aim to use historical time series data of the number of cases on different countries/regions to leverage insights into the causal impact of the policies applied by the governments. This analysis is applied on a country-by-country basis, and consists in

  • Comparing the growth rate in the number of cases before and after a restrictive policy is applied
  • Determining when a significant change in the growth rate is measured
  • Correlate (possibly with a delay) policy enactment with growth rate change
  • Rank countries according to the effectiveness of their policies

The Team

The Code

The code of this dashboard is open-source and available on GitHub.

Data Sources

Built With

  • jupyter-notebook
  • r
  • shiny
+ 8 more
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