The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people.
Abstract:In modern Volleyball, the block action differentiates the world-class teams. The purpose of this study is to understand what determines the action of the middle blocker in the moment that precedes the technical procedure of the block. The sample consisted of n4895 actions from 24 middle blockers, representing 30 footages of games from the 1st male volleyball division in the Portuguese league on season 2013/2014. We have also recorded the type of setting (ball tempo) of the opposing setter and the area where the opposing attack occurred. The chi-squared test analysis allowed us to establish that there is a relationship between the actions and the attack zones (x² = 109.956; p ≤ 0.001), as well as between the actions and the type of setting, in each attack zone (x² = 3,523.678; p ≤ 0.001 in all of them). Thus, we have verified that the action performs block but does not make contact with the ball that is the most frequent. We have also established that there is a strong tendency for the middle blocker to attempt to carry out the block in zones 3 and 4, to the detriment of the attack performed in defensive zone.
The new Coronavirus, responsible for the COVID-19 disease, is the most discussed topic in the current days, and the forecast numbers of new cases and deaths are the most important source of data in governmental decision-making. The present work presents a prediction model with two different approaches concerning the input data, by using Artificial Neural Networks (ANN). The use of a substantial mitigation procedure adopted (mandatory use of masks) was experimented as an input to the network, in order to evaluate the improvement in the results. The ANN forecasting model was demonstrated to predict with higher accuracy within the next twenty days using the information about the mandatory use of face masks. The final results showed that the twenty days ahead forecasting was made with an error of 24,7% and 1,6% for the number of cumulative cases of infection and deaths for Brazil, and 37,9% and 33,8% for Portuguese time series, respectively.
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