  The paper studies different regression approaches for modeling COVID-19
spread and its impact on the stock market. The logistic curve model was used
with Bayesian regression for predictive analytics of the coronavirus spread.
The impact of COVID-19 was studied using regression approach and compared to
other crises influence. In practical analytics, it is important to find the
maximum of coronavirus cases per day, this point means the estimated half time
of coronavirus spread in the region under investigation. The obtained results
show that different crises with different reasons have different impact on the
same stocks. It is important to analyze their impact separately. Bayesian
inference makes it possible to analyze the uncertainty of crisis impacts.
