Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

[Predictions of three mathematical models related with the COVID-19 Vaccination Strategy in Spain. June 2021.].

The Ministry of Health has coordinated three studies that have estimated the impact of the COVID-19 Vaccination Strategy in Spain. The models aim to help how to establish priority population groups for vaccination, in an initial context of dose limitation. With the same epidemiological and vaccine information, the results of this three different mathematical models point in the same direction: combined with physical distancing, staggered vaccination, starting with the high risk groups, would prevent 60% of infections, 42% of hospitalizations and 60% of mortality in the population.

Comparing quantile regression methods for probabilistic forecasting of NO pollution levels.

High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting, as oposed to point-forecasting, is a family of techniques that allow for the prediction of the expected distribution function instead of a single future value.

Handwritten Geometrical Patterns in the Evaluation of Motor Symptoms in Psychotic Disorders.

The analysis of handwriting has been used in several contexts. For example, handwriting has shown to be of value in the study of motor symptoms in neurological and mental disorders. In the present work, the geometric analysis of handwriting patterns is proposed as a tool to evaluate motor symptoms in psychotic disorders.

Precise automatic classification of 46 different pollen types with convolutional neural networks.

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Many industries, including medical and pharmaceutical, rely on the accuracy of this manual classification process, which is reported to be around 67%. In this paper, we propose a new method to automatically classify pollen grains using deep learning techniques that improve the correct classification rates in images not previously seen by the models.