Modeling the Geospatial Evolution of COVID-19 using Spatio-temporal Convolutional Sequence-to-sequence Neural Networks

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 January 19th and February 5th 2021 Portugal was the country in the world with the largest incidence rate, with 14-day incidence rates per 100,000 inhabitants in excess of 1,000. 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.

Catering for Students' Well-being during COVID-19 Social Distancing: A Case Study from a University Campus

COVID-19 gave rise to discussions around designing for life during the pandemic, in particular related to health, leisure and education. In 2020, an online survey aimed at university students (N=225) pointed the authors to various challenges related to well-being in terms of studying, socializing, community, and safety during the COVID-19 pandemic. These results shaped the crowdsensing-enabled service design of a mobile application, Tecnico GO!, aimed at supporting students' well-being. Considering the constant changing context caused by the pandemic, we present a study conducted during the academic year 2021-2022 and if/how the App's features continue to respond to student's needs. The evaluation of the App focused on 12 semi-structured interviews and think-aloud protocols. Findings cluster around three themes: a) Supporting the study experience;b) Building a sense of community;c) Improving gamification for better participation. Discussion elaborates on the student's perceptions around well-being during pandemics. Students' insights of the App are overall positive and highlight that crowdsensing-enabled design does contribute to learning, community and safety, but the gamification as currently deployed does not. © 2022 ACM.