How Can We Be Ahead of COVID-19 Curve? A Hybrid Knowledge-Based and Modified Regression Analysis Approach for COVID-19 Tracking in USA

Abstract

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 has been neither contained nor defeated. In fact, there is a recent record surge in the number of confirmed new cases. The rational question is thus: why has it taken so long to date to forecast the trajectory of the spread? To this end, this chapter presents a new predictive Knowledge-based (KB) toolkit named CORVITT (Corona Virus Tracking Toolkit) and a modified linear regression model. This logical step assists the officials, organizations, and users to forecast the spread trajectory and accordingly make proactive rather than retroactive intervention decisions. 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 or data including the reproductive rate, disease methods, real time polymerase chain reaction cycle threshold, the virus structure and pathogenesis, etc. The chapter is a seed in an in-progress study that will broaden its scope by including additional parameters.