A HYBRID KNOWLEDGE-BASED AND MODIFIED REGRESSION ANALYSIS APPROACH FOR COVID-19 TRACKING IN USA

Since its appearance in 2019, the covid-19 virus deluged the world with unprecedented data in short time. Despite the countless worldwide pertinent studies and advanced technologies, the spread is neither contained nor defeated. In fact, there is a record surge in the number of confirmed new cases since July 2020. This article presents a new predictive Knowledge-based (KB) toolkit named CORVITT (Corona Virus Tracking Toolkit) and modified linear regression model. This hybrid approach uses the confirmed new cases and demographic data, implemented. CORVITT is not an epidemiological model, in the sense that it does not model disease transmission, nor does it use underlying epidemiological parameters like the reproductive rate. It forecasts the spread in order to assist the official to make proactive intervention.