Impact of the pandemic and its containment measures in Europe upon aspects of affective impairments: a Google Trends informetrics study.

BACKGROUND: In late 2019, a new virus began spreading in Wuhan, China. By the end of 2021, more than 260 million people worldwide had been infected and 5.2 million people had died because of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Various countermeasures have been implemented to contain the infections, depending on the country, infection prevalence, and political and infrastructural resources. The pandemic and the containment measures have induced diverse psychological burdens. Using internet queries as a proxy, this study examines the psychological consequences on a European level of SARS-CoV-2 containment measures. METHODS: Using informetric analyses, this study reviews within 32 European countries a total of 28 search parameters derived from the International Statistical Classification of Diseases and Related Health Problems (ICD-10) as aspects of affective disorder. RESULTS: Our results show that there are several psychological aspects which are significantly emphasized during the pandemic and its containment measures: 'anxiety', 'dejection', 'weariness', 'listlessness', 'loss of appetite', 'loss of libido', 'panic attack', and 'worthlessness'. These terms are significantly more frequently part of a search query during the pandemic than before the outbreak. Furthermore, our results revealed that search parameters such as 'psychologist', 'psychotherapist', 'psychotherapy' have increased highly significantly (p < 0.01) since the pandemic. CONCLUSIONS: The psychological distress caused by the pandemic correlates significantly with the frequency of people searching for psychological and psychotherapeutic support on the Internet.

Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study.

BACKGROUND: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. OBJECTIVE: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. METHODS: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann-Whitney U tests. RESULTS: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. CONCLUSIONS: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.