establish relationship and trust with patient groups,
increase literacy about data security, ethics, and privacy concerns when using digital devices in affected populations, and
raise awareness about mental health in individuals and their families affected by neuropediatric diagnosis leading to severe disability.
Learning about a life-threatening disease has consequences for mental health. For example, parents may experience a traumatic event when they receive a neuropediatric diagnosis leading to severe disability in their child, such as progressing muscular dystrophy. The scientific literature on the mental health consequences of traumatic events, including our own work, provides robust evidence that many people show mental health resilience in this context. However, psychological distress is pervasive and symptoms of depression, anxiety, and stress are common in affected persons, including patients and the entire family, especially when the disease unfolds over time. Furthermore, these symptoms vary across a number of factors and socio-demographic characteristics, with e.g., avoidance coping, poor social support, or little socio-economic resources being associated with mental health problems.
The promises of the digital transformation include that affected populations now have access to communication tools allowing for an easy exchange of information and self help. However, these new tools also introduce new problems of data security, ethical, and privacy concerns that are not obvious at first sight.
With this project we aim to build trust with patient groups in order to prepare for a larger study that uses digital big data to assess the distribution and causes of mental health problems in the affected population so that we may intervene to improve health and wellbeing.
The project is sponsored by the Digital Society Initiative (DSI) and seeks to advance existing bot detection methodologies in social media by applying a combination of geographic trajectory and emotion analysis to explore bot detection from Twitter that later could help in improved public health surveillance. BOTS will deliver crucial knowledge about the nature of bot tweets (type, geographic trace, and emotive composition) that can be investigated in relation to bot tweets' volume by topic, compared to user-generated content and their impact on users' subsequent activity. Furthermore, having a better understanding about bot activity in social media (qualitatively and quantitatively) will help to more precisely (with and without bot accounts) identify geographic areas (and populations) exhibiting high concentrations (hotspots) of specific emotions or stress, as well as track the spread and distributional dynamics of stress related content (e.g. social contagion) in real time.
Is it possible to detect SI in tweets using our analysis approach and to which degree?
How are SI tweets distributed in regards to space, time, and sociodemographic characteristics of the twitter user and the space within which the tweets are published?
What are the emotions most often contained in SI tweets and do they differ in frequency and valence to the emotions contained in non-SI tweets? Is stress more common in SI tweets compared with non-SI tweets?
Is there a significant regional correlation between SI incidence in Twitter messages and actual suicide rates? Is there an underlying spatial logic which can help explain a detected trend?
Worldwide, 800,000 people commit suicide every year. That is one person every 40 seconds, making suicide a public health priority. A key aspect of prevention is addressing suicidal thoughts before they become an accomplished act. However, these thoughts are not often expressed, and are thus difficult to detect and measure. With more than half of the global population (59%) now having access to the internet and internet users spending an average of 6,5 hours online each day2, social media are also becoming ubiquitous across sociodemographic groups (Perrin 2015). Social media are profoundly reshaping social life and social interactions, and impacting interindividual communication. Social media are also increasingly used to express psychological distress and its relationship with subjectively perceived and/or objective stressful circumstances that may contribute to suicidal ideation and suicide. The study of communication through social media is a promising strategy to capture suicidal thoughts, which would otherwise remain undetected.
According to the United Nations Office for Disaster Risk Reduction (UNDRR), the indirect economic losses caused by climate-related disasters increased by over 150 % during 1998–2017 compared to the period 1978– 1997. Among the most prominent high-impact weather events are flooding, storms, and heatwaves. Scientists need to improve the accuracy and communication of weather forecasting to reduce or even avoid the damage caused by those kinds of weather hazards. To know even a few hours in advance about the place and intensity of weather hazards can help to mitigate the consequences of natural disasters, save lives, and prevent economic losses.
Detect and characterize social networks and groups that could be presenting social media for the specific scope of severe weather events
Develop a visual interactive tool aligned with current efforts of MeteoSwiss that can:
inform about weather conditions,
gather volunteer observations about the weather conditions, and
answer users’ questions related to weather and climate issues
The project is sponsored by the Digital Society Initiative (DSI) and aims at creating an interdisciplinary database and geovisual analytics tool to learn from infectious disease outbreaks in Switzerland, ca. 1850-1950.
The emergence of epidemics is a challenge for public health as illustrated by the current Covid-19 pandemic. Health policy makers benefit from historical experience to increase risk awareness and inform decision making (historical epidemiology). In this course, the reconstruction of regional nuances is important for understanding how epidemics spread in the population to prevent ecological fallacy.
Valuable experiences from the past, however, are not sufficiently accessible for researchers, policy makers, teachers, and the interested public. In particular, the digital society is running danger to have a historical blind spot because analogue information is forgotten in the archives. For two diseases in particular, the historical information in the archives is rich and largely unused in Switzerland: Cholera (1850s/1860s) & influenza (1889/1890 & 1918/1919).
Digitize examples of past outbreak experiences (quantitative and qualitative information) of Cholera (1850s/1860s) & influenza (1889/1890 & 1918/1919)
Develop an interdisciplinary online dash board (database and geovisual analytics tool) for a) researchers (open access data and code), b) teachers/students (teaching resources and MSc projects filling the database with new data), and c) science communication (data- and geo-visualisation for policy makers and the society)
Public Health Interventions, Epidemic Growth, and Regional Variation of the 1918 Influenza Pandemic Outbreak in a Swiss Canton and Its Greater Regions. https://doi.org/10.7326/M20-6231
The utilisation of medical services varies considerably both over time and between regions. Reasons may include medical progress, people’s different needs and the varying importance assigned to preventive measures. Major regional differences may indicate underprovision or overprovision of healthcare to certain population groups. This has significant repercussions for population health and for healthcare costs – particularly where chronic diseases are concerned.
The aim of the study is to identify and describe regional differences in the treatment of chronic diseases. In particular, it will look at how clinical guidelines and recommendations affect the choice of treatment and how their usefulness can be increased.
Are weak or negative clinical recommendations associated with higher geographical variation in utilisation than strong or positive recommendations? Cross-sectional study of 24 healthcare services. https://bmjopen.bmj.com/content/11/5/e044090
We invite interested research scholars for future research collaborations based on this data source. We further want to encourage young scholars and welcome MSc proposals using this data.
Possible research includes for example, to investigate relationships between the environment (green spaces, traffic noise, etc.) and mental health (emotional reactions, stress), to study mobility patterns over the course of days, weeks, or months (activity spaces, internal displacement, international migration), to develop language-specific algorithms to detect emotions and stress across languages posts (e.g. German, French, or Italian language, etc), or to identify geographic areas (and populations) exhibiting high concentrations (hotspots) of specific emotions or stress. Future studies can further test the feasibility for large-scale mental health surveillance based on these big geo-social media data investigating also symptoms of depression or anxiety that may inform timely interventions in populations with health care need.