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Common compartmental modeling for COVID-19 is based on a priori knowledge andnumerous assumptions. Additionally, they do not systematically incorporateasymptomatic cases. Our study aimed at providing a framework for data-drivenapproaches, by leveraging the strengths of the grey-box system theory orgrey-box identification, known for its robustness in problem solving underpartial, incomplete, or uncertain data. Empirical data on confirmed cases anddeaths, extracted from an open source repository were used to develop theSEAIRD compartment model. Adjustments were made to fit current knowledge on theCOVID-19 behavior. The model was implemented and solved using an OrdinaryDifferential Equation solver and an optimization tool. A cross-validationtechnique was applied, and the coefficient of determination $R^2$ was computedin order to evaluate the goodness-of-fit of the model. %to the data. Keyepidemiological parameters were finally estimated and we provided the rationalefor the construction of SEAIRD model. When applied to Brazil's cases, SEAIRDproduced an excellent agreement to the data, with an %coefficient ofdetermination $R^2$ $\geq 90\%$. The probability of COVID-19 transmission wasgenerally high ($\geq 95\%$). On the basis of a 20-day modeling data, theincidence rate of COVID-19 was as low as 3 infected cases per 100,000 exposedpersons in Brazil and France. Within the same time frame, the fatality rate ofCOVID-19 was the highest in France (16.4\%) followed by Brazil (6.9\%), and thelowest in Russia ($\leq 1\%$). SEAIRD represents an asset for modelinginfectious diseases in their dynamical stable phase, especially for new viruseswhen pathophysiology knowledge is very limited.

From SIR to SEAIRD: a novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19