**5. Conclusions**

*Some RNA Viruses*

**32**

**Figure 3.**

*(a): The trajectory of cases' dispersion over time in various US States. (b): A satisfactory agreement between* 

*the forecasted and actual data. (c). The growth and deterioration distributions of cases over time.*

As the enormity of the COVID-19 threat has become clear, the characteristics of existing COVID-19 complex analytic methodologies and the all-encompassing approach place serious limitations on their usefulness for practical use. The computer technologies have reached what no one could imagined, and the KB systems have proven very beneficial in many fields. The rational question is: why has it taken so long for a logical approach to appear to practicalize the analytical complex simulations? To answer the question, this chapter introduces machine smartness to assist humans' intelligence to capture the big picture of the virus illusiveness thus take proactive rather than retroactive steps to mitigate safely its inevitable adverse effects. This seed study introduced a hybrid KB-regression analysis model for COVID-19 forecasting. It used data collected from eleven US states at macro-level level to foresee the short-term spread trajectory. The outputs unveiled new discoveries and shed light on various facets of the COVID-19 in each state. The accuracy of the hybrid approach was gauged by comparing forecasted and actual data and satisfactory agreements were found. It should be noted that this study is a step forward, but additional development is in progress for improvement preparations.
