IDMON - covID MOdel Network

Investigating relevant metadata to measure impact of Covid19 policies and decision making

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Inspiration

During the Covid19 pandemic we witnessed a number of different policies (regarding e.g. prevention measures) among all countries. An indicative example was Sweden (no prevention measures at all) compared to Italy (lockdown measures) although there was a growing concern among its citizens.

We wonder: Was their strategy successful or not?

What we plan to do

  • Accumulate data from the existing datasets (based on existing covid19 strategies across EU countries),
  • Identify patterns among the datasets and graphic representation of interventions
  • Make a timeline analysis of prevention measures and effectiveness estimation based on the number of total cases/deaths due to the pandemic.
  • By developing a country-agnostic model to be used by policy makers across EU to fit their respective countries in case of a new or recurring pandemic.

Challenges we ran into

  • Collecting sufficiently detailed and complete datasets.
  • Making meaningful connections among identified parameters.

Accomplishments that we're proud of

We are proud to have been able to formulate and present a complex plan quickly in a meaningful and user-friendly way for our intended audience (e.g. public health policy makers, data analytic companies). Inclusion of a preliminary data analysis based on different programming environments is considered one of our strengths given the time limit of the hackathon.

What we learned

Focusing on a broader concept (policies) rather than a narrow one (e.g. personal intervention) would benefit more people in cases of a (new) pandemic on the long term. Even with different strategies implemented in the EU countries, we witnessed that outliers (e.g. people not complying to restriction regulations) would still remain, therefore different strategies can and should be tested, so as every country can witness their results

What's next for IDMON

  • Further develop a country-agnostic index framework
  • Enrich the current datasets
  • Include machine learning and simulation models to refine the results
  • Involve policy makers for refining the model’s parameters

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