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• In this article, we systematically reviewed and analyzed COVID-19 trends, policies and vital epidemiological indicators, quantifying the impact of COVID-19 on electricity consumption. • We introduced a data-driven approach to predict the electricity consumption in non-COVID-19 scenario. • We bridged the knowledge gap between key energy and epidemiological parameters, finding a statistic correlation between energy loss and effective reproductive number (R t). In addition to the tremendous loss of life due to coronavirus disease 2019 (COVID-19), the pandemic created challenges for the energy system, as strict confinement measures such as lockdown and social distancing compelled by governments worldwide resulted in a significant reduction in energy demand. In this study, a novel, quantitative and uncomplex method for estimating the energy consumption loss due to the pandemic, which was derived from epidemiological data in the beginning stages, is provided; the method bonds a data-driven prediction (LSTM network) of energy consumption due to COVID-19 to an econometric model (ARDL) so that the long- and short-term impact can be synthesized with adequate statistical validation. The results show that energy loss is statistically correlated with the time-changing effective reproductive number (R t) of the disease, which can be viewed as quantifying confinement intensity and the severity of the earlier stages of the pandemic. We detected a 1.62% decrease in electricity consumption loss caused by each percent decrease in R t on average. We verify our method by applying it to Germany and 5 U.S. states with various social features and discuss implications and universality. Our results bridge the knowledge gap between key energy and epidemiological parameters and provide policymakers with a more precise estimate of the pandemic's impact on electricity demand so that strategies can be formulated to minimize losses caused by similar crises. [ABSTRACT FROM AUTHOR]