COVID-19 Time Series Forecasting - Twenty Days Ahead.

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.

Analysis and Forecasting Incidence, Intensive Care Unit Admissions, and Projected Mortality Attributable to COVID-19 in Portugal, the UK, Germany, Italy, and France: Predictions for 4 Weeks Ahead.

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.

Isokinetic knee strength qualities as predictors of jumping performance in high-level volleyball athletes: multiple regression approach.

BACKGROUND: Previous investigations noted potential importance of isokinetic strength in rapid muscular performances, such as jumping. This study aimed to identify the influence of isokinetic-knee-strength on specific jumping performance in volleyball. The secondary aim of the study was to evaluate reliability and validity of the two volleyball-specific jumping tests. METHODS: The sample comprised 67 female (21.96±3.79 years; 68.26±8.52 kg; 174.43±6.85 cm) and 99 male (23.62±5.27 years; 84.83±10.37 kg; 189.01±7.21 cm) high- volleyball players who competed in 1st and 2nd National Division. Subjects were randomly divided into validation (N.=55 and 33 for males and females, respectively) and cross-validation subsamples (N.=54 and 34 for males and females, respectively). Set of predictors included isokinetic tests, to evaluate the eccentric and concentric strength capacities of the knee extensors, and flexors for dominant and non-dominant leg. The main outcome measure for the isokinetic testing was peak torque (PT) which was later normalized for body mass and expressed as PT/Kg. Block-jump and spike-jump performances were measured over three trials, and observed as criteria. Forward stepwise multiple regressions were calculated for validation subsamples and then cross-validated. Cross validation included correlations between and t-test differences between observed and predicted scores; and Bland Altman graphics. RESULTS: Jumping tests were found to be reliable (spike jump: ICC of 0.79 and 0.86; block-jump: ICC of 0.86 and 0.90; for males and females, respectively), and their validity was confirmed by significant t-test differences between 1st vs. 2nd division players. Isokinetic variables were found to be significant predictors of jumping performance in females, but not among males. In females, the isokinetic-knee measures were shown to be stronger and more valid predictors of the block-jump (42% and 64% of the explained variance for validation and cross-validation subsample, respectively) than that of the spike-jump (39% and 34% of the explained variance for validation and cross-validation subsample, respectively). Differences between prediction models calculated for males and females are mostly explained by gender-specific biomechanics of jumping. CONCLUSIONS: Study defined importance of knee-isokinetic-strength in volleyball jumping performance in female athletes. Further studies should evaluate association between ankle-isokinetic-strength and volleyball-specific jumping performances. Results reinforce the need for the cross-validation of the prediction-models in sport and exercise sciences.