Agent-based simulations for protecting nursing homes with prevention and vaccination strategies.

Due to its high lethality among older people, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures and vaccines becoming available at scale, nursing homes might relax prohibitory measures while controlling the spread of infections. By control we mean that each index case infects less than one other person on average. Here, we develop an agent-based epidemiological model for the spread of SARS-CoV-2 calibrated to Austrian nursing homes to identify optimal prevention strategies. We find that the effectiveness of mitigation testing depends critically on test turnover time (time until test result), the detection threshold of tests and mitigation testing frequencies. Under realistic conditions and in absence of vaccinations, we find that mitigation testing of employees only might be sufficient to control outbreaks if tests have low turnover times and detection thresholds. If vaccines that are 60% effective against high viral load and transmission are available, control is achieved if 80% or more of the residents are vaccinated, even without mitigation testing and if residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular testing and sequencing of virus genomes is advised to enable early identification of new variants of concern.

Assessment of the effectiveness of Omicron transmission mitigation strategies for European universities using an agent-based network model.

BACKGROUND: Returning universities to full on-campus operations while the COVID-19 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts and adoption of non-pharmaceutical intervention measures (NPIs). Due to the generalised academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight to base these decisions on. METHODS: To address this problem, we analyse a calibrated, data-driven agent-based simulation of transmission dynamics of 13,284 students and 1,482 faculty members in a medium-sized European university. We use a co-location network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focus on actionable interventions that are part of the already existing decision-making process of universities to provide guidance for concrete policy decisions. RESULTS: Here we show that, with the Omicron variant of the SARS-CoV-2 virus, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks given the vaccination coverage of about 85% recently reported for students in Austria. CONCLUSIONS: Our results show that controlling the spread of the virus with available vaccines in combination with NPIs is not feasible in the university setting if presence of students and faculty on campus is required.

Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data.

We aim to identify those measures that effectively control the spread of SARS-CoV-2 in Austrian schools. Using cluster tracing data we calibrate an agent-based epidemiological model and consider situations where the B1.617.2 (delta) virus strain is dominant and parts of the population are vaccinated to quantify the impact of non-pharmaceutical interventions (NPIs) such as room ventilation, reduction of class size, wearing of masks during lessons, vaccinations, and school entry testing by SARS-CoV2-antigen tests. In the data we find that 40% of all clusters involved no more than two cases, and 3% of the clusters only had more than 20 cases. The model shows that combinations of NPIs together with vaccinations are necessary to allow for a controlled opening of schools under sustained community transmission of the SARS-CoV-2 delta variant. For plausible vaccination rates, primary (secondary) schools require a combination of at least two (three) of the above NPIs.

Dashboard of Sentiment in Austrian Social Media During COVID-19.

To track online emotional expressions on social media platforms close to real-time during the COVID-19 pandemic, we built a self-updating monitor of emotion dynamics using digital traces from three different data sources in Austria. This allows decision makers and the interested public to assess dynamics of sentiment online during the pandemic. We used web scraping and API access to retrieve data from the news platform derstandard.at, Twitter, and a chat platform for students. We documented the technical details of our workflow to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allowed us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We used special word clouds to visualize that overall difference. Longitudinally, our time series showed spikes in anxiety that can be linked to several events and media reporting. Additionally, we found a marked decrease in anger. The changes lasted for remarkably long periods of time (up to 12 weeks). We have also discussed these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online at http://www.mpellert.at/covid19_monitor_austria/. Our work is part of a web archive of resources on COVID-19 collected by the Austrian National Library.

Impact of the COVID-19 Pandemic on Mental Health among 157,213 Americans.

BACKGROUND: The coronavirus (COVID-19) pandemic presents an unprecedented crisis with potential negative mental health impacts. METHODS: This study used data collected via Youper, a mental health app, from February through July 2020. Youper users (N = 157,213) in the United States self-reported positive and negative emotions and anxiety and depression symptoms during the pandemic. We examined emotions and symptoms before (pre), during (acute), and after (sustained) COVID-related stay-at-home orders. RESULTS: For changes in frequency of reported acute emotions, from the pre to acute periods, anxiety increased while tiredness, calmness, happiness, and optimism decreased. From the acute to sustained periods, sadness, depression, and gratitude increased. Anxiety, stress, and tiredness decreased. Between the pre and sustained periods, sadness and depression increased, as did happiness and calmness. Anxiety and stress decreased. Among symptom measures, anxiety increased initially, from the pre to the acute periods, but later returned to baseline. LIMITATIONS: The study sample was primarily comprised of young people and women. The app does not collect racial or ethnicity data. These factors may limit generalizability. Sample size was also not consistent for all data collected. CONCLUSIONS: The present study suggests that although there were initial negative impacts on emotions and mental health symptoms in the first few weeks, many Americans demonstrated resilience over the following months. The impact of the pandemic on mental health may not be as severe as predicted, although future work is necessary to understand longitudinal effects as the pandemic continues.

Complexity, transparency and time pressure: practical insights into science communication in times of crisis

A global crisis such as the COVID-19 pandemic that started in early 2020 poses significant challenges for how research is conducted and communicated. We present four case studies from the perspective of an interdisciplinary research institution that switched to "corona-mode" during the first two months of the crisis, focussing all its capacities on COVID-19-related issues, communicating to the public directly and via media, as well as actively advising the national government. The case studies highlight the challenges posed by the increased time pressure, high demand for transparency, and communication of complexity and uncertainty. The article gives insights into how these challenges were addressed in our research institution and how science communication in general can be managed during a crisis.

A structured open dataset of government interventions in response to COVID-19.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.