Mobility and COVID-19 in Andorra: Country-scale analysis of high-resolution mobility patterns and infection spread

Graph Attention-Based Curriculum Learning for Mental Healthcare Classification

Current research has examined the use of user-generated data from online media to identify and diagnose depression as a serious mental health issue that can significantly impact an individual's daily life. To this end, many studies examined words in personal statements to identify depression. In addition to aiding in the diagnosis and treatment of depression, this study uses and utilizes a Graph Attention Network (GAT) model for the classification of depression from online media. The model is based on masked self-attention layers, that assigns different weight to each node in a neighborhood without costly matrix operations. In addition, an emotion lexicon was extended using hypernyms to improve the model performance. Furthermore, embedding of the model was used to illustrate the contribution of the activated words to each symptom and to obtain qualitative agreement from psychiatrists. This technique uses previously learned embedding to illustrate the contribution of activated words to depressive symptoms in online forums. A significant improvement was observed in the model's performance through the use of the lexicon extension method, resulting in an increase in the ROC performance. The performance was also enhanced by an increase in vocabulary and the adoption of a graph-based curriculum. The lexicon expansion method involves the generation of additional words with similar semantic attributes, utilizing similarity metrics to reinforce lexical features. The graph-based curriculum learning also utilized to handle more challenging training samples, allowing the model to develop increasing expertise in learning complex correlations between input data and output labels.

Leisure mobility changes during the COVID-19 pandemic - An analysis of survey and mobile phone data in Sweden

The COVID-19 pandemic affected travelling in general, and the leisure mobility and the spatial distribution of travellers in particular. In most parts of the world, both domestic and international travel has been replaced by restrictive policies and recommendations on mobility. A modal shift from public transport towards private cars and micro-mobility was also observed. This study seeks to trace the implications of the COVID-19 pandemic for leisure mobility. We use a unique Swedish database containing daily mobility patterns of pseudonymised mobile phone users, combined with a survey on vacation transport behaviour. By contrasting mobility patterns for selected holiday days during the unaffected summer of 2019 with corresponding dates in 2020 and 2021, we are able to model and detect the pandemic effects on tourism and recreational mobility. Moreover, by identifying the general mobility patterns, we analyse whether and how the transport mode has changed. Using data on the spatial distribution of recreational amenities, we identify locations that were favoured during the pandemic. In Sweden, even though the pandemic decreased in spread and severity during the summers, most travel restrictions were still enforced, international vacations uncommon, and larger vacation spots, such as amusement parks and cultural institutions, were closed down. Swedish vacation homes in remote or rural areas were quickly booked. This change in recreational behaviour, where less populated areas, open air and nature recreation were favoured over indoor or crowded urban cultural activities, was more substantial in 2021 than in 2020. This result shows how policies can effectively be developed, so that Swedes respond properly to recommendations and adjust their vacation plans.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant — Shanghai Municipality, China, 2022

What is already known about this topic?

Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale.

What is added by this report?

This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions.

What are the implications for public health practice?

Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.