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Contact tracing has become one of the most useful tools for fighting the novel Corona Virus (COVID-19) pandemic worldwide. The underlining philosophy of contact tracing is determining people who have been in contact with infected persons and thus isolate them from becoming agents of onward transmission of the virus. Slow tracing of contacts and inconsistent or inaccurate information provided by patients usually leads to the spread of the virus along a trajectory at the healthcare systems' blindside. This has led to the proposal of app-based contact tracing solutions. This paper proposes an SQL-based framework that transforms simple interaction data entries into interaction graphs and applies graph theory to prioritize the contact tracing process. The framework returns nodes or individual IDs together with values called Risk_Points to enable individuals' selection for isolation and treatment. Results on simulated data show that the proposed framework can help slow the virus's rate of transmission.
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