Provide decision making tools to assist the hospitals in the allocation of key resources during the times of scarcity of medical equipments

What it does

The system knows where key resources are available or will be available by when.  The system allows simple queries to help find the nearest and best available resources

How we built it


  • Identified data resources regarding demographics and current COVID situation in Switzerland.
  • Developed scripts to process the data.
  • Approached healthcare decision makers.
  • Reviewed the accuracy of existing projection modelling approaches.
  • Used relatively high-level SIR models based high level parameters related to COVID transmission
  • Demonstrated the use of an optimisation algorithm to a hypothetical problem.

Challenges we ran into

Our work during this hackathon highlighted the following challenges:

  • There is a lack of reliable open data in this area and reported findings often lack clarity.
  • We experienced reluctance from key parties to share up to date information about resource availability.
  • Relevant assistance was scarce during the Hackathon process and key authorities were unavailable to provide information.
  • Limited time and resources.

Although there are good reasons for all of the above, it complicated our efforts. We look forward to addressing these difficulties in the future by proving the use of our model, gaining buy-in from the health care community and using improved information to produce a more robust model.

Accomplishments that we're proud of

A working framework built within a very short time to address the challenges

What we learned

  • In the limited time available it was difficult to establish a full-functioning framework for immediate deployment but we were able to produce a prototype and test it under pressure to the system (2x current demand) and the model provided reasonable results.
  • Our investigation has led to more questions than answers; however,  we made significant progress in developing a model framework that can be extended to address many of the highlighted challenges.
  • This will be an ongoing effort and we aim to maintain and expand this collaboration in the future.

What's next

Future efforts will be focused on improving the input data. To achieve this, we will seek collaboration with data producers to establish ways to obtain the necessary data.  We will also aim to collaborate with healthcare professionals to expand the applicability of the solutions developed.  Potential expansions to our model include 1) establishing links between traditional epidemiology and demographics to identify vulnerable populations and predict outbreaks, 2) extending the optimisation framework to include forecast of new cases

Try It out




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