The SARS-CoV-2 is a virus that produces a respiratory disease known as coronavirus 2019(COVID-19). This belongs to the family of coronaviruses, being a type of virus that infects humansand some animals around the world. In fact, SARS-CoV-2 infection was identified for the first timein people exposed to a seafood market in the Wuhan sector, China, around November 2019. Since then, it has been one of the biggest public health problems in the history of mankind inrecent centuries, since this virus has a rapid rate of transmission and contagion. Thus, the WorldHealth Organization (WHO) issued a state of pandemic in a matter of a few months, causing greateconomic losses worldwide. Given the dimension of this problem, interest has been aroused by dif-ferent scientific teams to identify the behavior of the spread, to find a relevant way for its treatment.
By virtue of this situation, in this degree project an emergent method driven by data will bepresented, called Dynamic Mode Decomposition (DMD), which will allow to know the epidemiolo-gical dynamics in a spatio-temporal way over a short period of time, using the reportsgenerated within the metropolitan area of Santiago de Cali, Colombia. In this sense, thecomputational tools MATLAB and R will be used to run the different simulations that will beuseful to understand the behavior of this virus; Likewise, complementary, and auxiliary conceptsthat will be relevant to have an adequate handling of the DMD method will be analyzed, such asSingular Value Decomposition (SVD), Principal Component Analysis (PCA), Independent Com-ponent Analysis (ICA) and finally the Koopman Analysis.
Keywords: Dynamic Mode Decomposition, Coronavirus, DMD, COVID-19, SARS-CoV-2.
