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On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried COVID-19 Growth Patterns Comparison: ARIMA and Gompertz Models Rambam Maimonides Medical Journal 2 July 2020  Volume 11  Issue 3  e0022 out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21-May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R 2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R 2 >0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries' health care systems do not collapse.

Nowadays, policies are being developed in many countries in order to decrease their greenhouse gases emissions. While in this area some technologies are widely installed (wind and solar energy), other ones, like the sea energy, could get an important role in the medium and long term. That is why the most relevant technologies associated to the sea energy conversion are presented in this paper: Tidal energy (both the traditional power plants and those based on tidal streams), wave energy and ocean thermal energy conversion. However, the sea energy conversion is not completely developed yet due to some unsolved technical problems, apart from their high cost. The most important advantages and disadvantages related to each kind of technology are also analyzed in this paper, comparing the main characteristics among them. Therefore, technological development government policies and the possibility of setting up a related industrial field will be the key actions to make possible the future of the sea energy. Solving the economical and technical problems, it will be possible to make good use of this alternative source of energy, with high energy density.

In this article,carry out an analysis on the growth and expansion models of the new virus outbreak belonging to the Coronavirus family called SARS-CoV-2 and associated with the clinical picture COVID-19. This outbreak of the disease was declared a pandemic by the World Health Organization (WHO) in March 2020, it is now affecting most countries in the world and its accelerated progress is a global concern. A non-linear growth model based on the Gompertz function was designed to characterize the serious pandemic impact in Asian and European countries: Spain, Italy, South Korea, and Hubei (China). The compilation of official data from each area on infected and deceased people until May 11, 2020, to verify the validity of the growth model in the calculated terms. With the obtained values, made a comparison by measuring the forecast errors, using the root indicators of the root mean square error (RMSE), the mean absolute percentage error (MAPE) and the regression coefficient index R 2 , which yielded highly accurate values of the predicted correlation for infected and dead of 0.99 on the dates considered. Verified the validation of the viral growth model of COVID-19 for these four countries and verified how the different measures taken to alleviate the pandemic have affected the final results of infected and dead countries, obtaining different growth coefficients that could be due to some exogenous factors (such as social, political and health factors, among others) that are difficult to measure and require qualitative methods and resources. The simple and well-structured model can be adapted to different propagation dynamics. Due to its direct and rapid implementation, the model could be useful for health managers and politicians for better decision-making in the control and prevention of this pandemic.