Europe was hit hard by the COVID-19 pandemic and Portugal was severely affected, having suffered three waves in the first twelve months. Approximately between Jan 19th and Feb 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge, since existing analytical methods fail to capture the complex dynamics that result from the contagion within a region and the spreading of the infection from infected neighboring regions. We use a previously developed methodology and official municipality level data from the Portuguese Directorate-General for Health (DGS), relative to the first twelve months of the pandemic, to compute an estimate of the incidence rate in each location of mainland Portugal. The resulting sequence of incidence rate maps was then used as a gold standard to test the effectiveness of different approaches in the prediction of the spatial-temporal evolution of the incidence rate. Four different methods were tested: a simple cell level autoregressive moving average (ARMA) model, a cell level vector autoregressive (VAR) model, a municipality-by-municipality compartmental SIRD model followed by direct block sequential simulation and a new convolutional sequence-to-sequence neural network model based on the STConvS2S architecture. We conclude that the modified convolutional sequence-to-sequence neural network is the best performing method in this task, when compared with the ARMA, VAR, and SIRD models, as well as with the baseline ConvLSTM model.

Cataract is a disease opacifying the crystalline, leading to a blurred vision and ultimately to blindness. With an aging population, the incidence of cataract is increasing, as well as the number of treatments. The solution available is its complete removal, followed by an implant of an intraocular lens (IOL). Although the post-operative complications on cataract surgeries have been decreasing in general, the bag-IOL complex dislocation is still an issue, probably being the most serious complication under this procedure. In this work, an axisymmetric Finite Element (FE) modelling strategy of the crystalline complex during the process of accommodation under cataract surgery is proposed. The goal was to understand the influence of biomechanical alterations promoted by the IOL on bag-IOL dislocation after surgery. An increase of force and stress in the zonules was verified in the pseudophakic eye compared to the complete eye, which could explain why zonules break years after surgery, leading to the bag-IOL dislocation. The axisymmetric FE model proposed in this work is innovative in this field, which still lacks detailed research, and can be an important complement for the clinical and biomechanical work on the crystalline complex.

We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform the original STConvS2S and other baseline models on tasks related to predicting future time-steps. In tests related to predicting a missing time-step, some of the proposed extensions also lead to improvements over the original STConvS2S architecture, although simpler models seem to be beneficial in this scenario. CCS CONCEPTS • Computing methodologies → Neural networks; Supervised learning by regression; • Applied computing → Earth and atmospheric sciences.