Covid-19 sure has taken everyone by surprise, and today's governments are still learning how to deal with this epidemy. It would have been great if we'd had learned, from past epidemics, a set of rules to follow in order to optimize the safety of the people. This is what we are trying to do in this projets. Understand the epidemic, how it will progress, how we can simulate its end in each country, and how each measure implemented by each country has had an effect on the progress of the covid-19.

How I built it

First, we start working on Switzerland's situation, and will extend to other countries once our model works. What interests us, is to see the effects of a given measure in the spread of the virus. We determine a set of measures: [0, ..., M-1], a category for people [kids, teen, student, young adult, adult, senior, elderly]. Think of a measure m, i.e. schools are closed. This measure will highly reduce the chance that a person from the category kids will infect someone from the category elderly. We will be running SIR models in order to compare the evolution of the virus based on the probability that someone from a category will infect someone from another category. This modelization will allow us to determine the most effective measure.

Currently working on

  • Implement different techniques to simulate an epidemic.
  • Visualize the evolution of the epidemy in each country as a timeline with marks when and which measures were implemented

Built With

  • epidemiology
  • python
  • seir
  • sir
  • statistics
  • stochastic
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