COVID-19 Tracking

Description This repository includes the experiment results, source code, and test data for Three Cs risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and HermiT. results.csv: The results of the experiment. test_cases.csv: The test cases of the experiment. CIRO.owl: The COVID-19 Infection Risk Ontology. test_main.py: The source code of the experiment. Requirements: Python 3 Owlready2 rdflib

We present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. SM-Covid-19 uses a NO-SQL data storage system to ensure scalability and performance. At regular intervals, SM-Covid-19 backend generates a complete dump of the dataset. The dump is converted into a relational database stored into a CSV formatted file to allow the open data to be easy to consult and process. The CSV file is structured as follows: PID1 and PID2 fields are pre-processed via SHA256 hash with a seed stored into the SoftMining backend system. The CSV is finally cleaned to remove duplicates. • Date of the contact (dd/MM/YYYY) • Time of the contact (HH:MM:SS) • PID1 (256-bit hex) • PID2 (256-bit hex) • Contact duration (Integer, in seconds) Contact distance (Float, in meters)