In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID-19. We create a simple model of contamination and show how vaccine and exposition can jointly affect the probability of being infected.


We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.

In the second part, we demonstrate how to update a model based on acquired knowledge. We refine our initial model (built in Part 1) based on prior knowledge. We show how easily information collected in news publications about cases numbers and vaccine efficacy can be integrated in the model. We show how this added information helps improve our original estimations on the probability of being infected. We also review how we can set specific nodes values and obtain the probability of being infected if vaccinated or not. This option allows to test scenarios.

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