epidemic_mitigation is a framework to test the perfomances of scoring algorithms that could be used to improve the identification of infected individuals using digital contact tracing data.
We employ realistic individual-based models (OpenABM-Covid19) to investigate a number of intervention strategies aiming at containing epidemic outbreaks, such as case-based measures (e.g. individual and household quarantine and mobility restrictions).
OpenABM-Covid19 is an agent-based model (ABM) developed by Oxford's Fraser group to simulate the spread of Covid-19 in a urban population.
An Intervention API that allows testing of risk assessment and quarantine strategies can be found at the following link: https://github.com/aleingrosso/OpenABM-Covid19.
In the following figure the evolution daily infected infected individuals are shown. Each day 200 tests are performed using the scoring algorithms. The best perfomances are obtained by the sib_ranker. For more information see paper.
- see the epi_mitigation notebook
- sib_rank - authors: sibyl-team
- mean_field_rank - authors: sphinxteam
Have a look to the template_ranker to design your class.
If you want to contribute write us (sibyl-team) or make a pull request.
The sibyl-team:
Alfredo Braunstein (alfredo.braunstein@polito.it), Alessandro Ingrosso (@ai_ingrosso), Indaco Biazzo (@ocadni), Luca Dall'Asta, Anna Paola Muntoni, Fabio Mazza, Giovanni Catania
This project has been partially funded by Fondazione CRT through call "La Ricerca dei Talenti", project SIBYL, and by the [SmartData@PoliTO] (http://smartdata.polito.it) center on Big Data and Data Science.
