  The main objective of this work is to test whether some stochastic models
typically used in financial markets could be applied to the COVID-19 pandemic.
To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models
originally designed for interest rate pricing but transformed by us into a
forecasting tool. For the latter, which we denoted CIR*, both the
Euler-Maruyama method and the Milstein method were used. Forecasts obtained
with the maximum likelihood method have been validated with 95\% confidence
intervals and with statistical measures of goodness of fit, such as the root
mean square error (RMSE). We demonstrate that the accuracy of the obtained
results is consistent with the observations and sufficiently accurate to the
point that the proposed CIR* framework could be considered a valid alternative
to the classical ARIMAX for modelling pandemics.
