The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is already the most worrying health problem worldwide. The virus spread rapidly across the countries, making many researchers looking for answers to mitigate the disease's effects. While vaccines are not available on a large scale, mathematical modeling has allowed the scientific community to perform forecasts for decision-making and social distancing policies to decrease the velocity of the COVID-19 transmission. However, dynamic models must represent the pandemic's reality considering validation criteria and an appropriate procedure that ensures realistic simulations. These principles are the only way to make an epidemiological model useful for studying and analyzing practical effects. From a control theory point of view, representative models ensure optimal solutions and allow a more reliable and robust control strategy. Therefore, this paper proposes a parameter identification algorithm for a dynamic pandemic disease model spread, including a new time-varying parameter for control applications. The developed framework uses an epidemiological model, denoted here as , capable of associating the real pandemic dynamics to its biological parameters and the population social mobility in real-time, which can be led by an appropriate optimal control strategy. Simulations and forecasts are performed comparing with official data of the epidemic in the United States.