Challenges, strategies and consequences for German nursing homes during the first wave of the COVID-19 pandemic. A qualitative interview study. (preprint)

Background The first wave of the COVID-19 pandemic reached Germany between March and May 2020. In order to contain the spread of the virus and particularly protect vulnerable people, the government imposed a lockdown in March 2020. In addition to infection control measures, such as hygiene and social distancing requirements, a general ban on access to nursing homes for relatives and external service providers was issued.Methods To investigate the challenges and consequences of the enacted infection prevention measures and specific strategies for nursing homes in Germany, a multicentre cross-sectional qualitative interview study with nursing home managers and ward managers was conducted. Recorded audio data were transcribed, analysed using thematic framework analysis and reflected in peer debriefings.Results 78 interviews with 40 nursing home managers and 38 ward managers from 43 German nursing homes were conducted. At organisational level, appointing a multi-professional crisis task force, reorganizing the use of building and spatial structures, continuous adaption and implementation of hygiene plans, adapting staff deployment to dynamically changing demands, managing additional communicative demands and relying on and resorting to informal networks were topics identified in response to the challenges posed by the pandemic. At direct care level, changed routines, taking over non-nursing tasks, increased medical responsibility, increased documentation demands, promoting social participation and increased communication demands were identified as topics in dealing with the challenges of the COVID-19 pandemic. Also various negative consequences were identified, such as psychological stress and negative emotional consequences. Positive emotional consequences such as a newly established team cohesion, the feeling of a calm atmosphere and a stronger sense of connection between nursing staff and residents were also reported.Conclusions The results of the described challenges, strategies and consequences allow recommendations as basis for possible approaches and successful adaptation processes in nursing home care in the future. There is also a need for local networks to act in a coordinated way and a need for quantitative and qualitative support for nurses, such as staff support as well as advanced nursing practice, to cope with the challenges of the pandemic.

Advanced NO2 retrieval technique for the Brewer spectrophotometer applied to the 20-year record in Rome, Italy

A re-evaluated data set of nitrogen dioxide (NO2) column densities over Rome for the years 1996 to 2017 is here presented. This long-term record is obtained from ground-based direct sun measurements with a MkIV Brewer spectrophotometer (serial number #067) and further reprocessed using a novel algorithm. Compared to the original Brewer algorithm, the new method includes updated NO2 absorption cross sections and Rayleigh scattering coefficients, and it accounts for additional atmospheric compounds and instrumental artefacts, such as the spectral transmittance of the filters, the alignment of the wavelength scale, and internal temperature. Moreover, long-term changes in the Brewer radiometric sensitivity are tracked using statistical methods for in-field calibration. The resulting series presents only a few (about 30) periods with missing data longer than 1 week and features NO2 retrievals for more than 6100 d, covering nearly 80 % of the considered 20-year period. The high quality of the data is demonstrated by two independent comparisons. In the first intensive campaign, Brewer #067 is compared against another Brewer (#066), recently calibrated at the Izaña Atmospheric Observatory through the Langley method and there compared to reference instrumentation from the Network for the Detection of Atmospheric Composition Change (NDACC). Data from this campaign show a highly significant Pearson's correlation coefficient of 0.90 between the two series of slant column densities (SCDs), slope 0.98 and offset 0.05 DU (Dobson units;1.3×1015 molec.cm-2). The average bias between the vertical column densities is 0.03 DU (8.1×1014 molec.cm-2), well within the combined uncertainty of both instruments. Brewer #067 is also independently compared with new-generation instrumentation, a co-located Pandora spectrometer (#117), over a 1-year-long period (2016–2017) at Sapienza University of Rome, showing linear correlation indices above 0.96 between slant column densities, slope of 0.97, and offset of 0.02 DU (5.4×1014 molec.cm-2). The average bias between vertical column densities is negligible (-0.002 DU or -5.4×1013 molec.cm-2). This, incidentally, represents the first intercomparison of NO2 retrievals between a MkIV Brewer and a Pandora instrument. Owing to its accuracy and length, the Brewer data set collected in Rome can be useful for satellite calibration/validation exercises, comparison with photochemical models, and better aerosol optical depth estimates (NO2 optical depth climatology). In addition, it can be employed to identify long-term trends in NO2 column densities in a metropolitan environment, over two decades witnessing important changes in environmental policies, emission loads and composition, and the effect of a worldwide economic recession, to offer just a few examples. The method can be replicated on the more than 80 MkIV spectrophotometers operating worldwide in the frame of the international Brewer network. The NO2 data set described in this paper can be freely accessed at 10.5281/zenodo.4715219 .

SARS-CoV-2 variants B.1.351 and B.1.1.248: Escape from therapeutic antibodies and antibodies induced by infection and vaccination (preprint)

SUMMARY The global spread of SARS-CoV-2/COVID-19 is devastating health systems and economies worldwide. Recombinant or vaccine-induced neutralizing antibodies are used to combat the COVID-19 pandemic. However, recently emerged SARS-CoV-2 variants B.1.1.7 (UK), B.1.351 (South Africa) and B.1.1.248 (Brazil) harbor mutations in the viral spike (S) protein that may alter virus-host cell interactions and confer resistance to inhibitors and antibodies. Here, using pseudoparticles, we show that entry of UK, South Africa and Brazil variant into human cells is susceptible to blockade by entry inhibitors. In contrast, entry of the South Africa and Brazil variant was partially (Casirivimab) or fully (Bamlanivimab) resistant to antibodies used for COVID-19 treatment and was less efficiently inhibited by serum/plasma from convalescent or BNT162b2 vaccinated individuals. These results suggest that SARS-CoV-2 may escape antibody responses, which has important implications for efforts to contain the pandemic.

Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic (preprint)

Social media analysis has become a common approach to assess public opinion on various topics, including those about health, in near real-time. The growing volume of social media posts has led to an increased usage of modern machine learning methods in natural language processing. While the rapid dynamics of social media can capture underlying trends quickly, it also poses a technical problem: algorithms trained on annotated data in the past may underperform when applied to contemporary data. This phenomenon, known as concept drift, can be particularly problematic when rapid shifts occur either in the topic of interest itself, or in the way the topic is discussed. Here, we explore the effect of machine learning concept drift by focussing on vaccine sentiments expressed on Twitter, a topic of central importance especially during the COVID-19 pandemic. We show that while vaccine sentiment has declined considerably during the COVID-19 pandemic in 2020, algorithms trained on pre-pandemic data would have largely missed this decline due to concept drift. Our results suggest that social media analysis systems must address concept drift in a continuous fashion in order to avoid the risk of systematic misclassification of data, which is particularly likely during a crisis when the underlying data can change suddenly and rapidly.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic (preprint)

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

3D reconstruction of SARS-CoV-2 infection in ferrets emphasizes focal infection pattern in the upper respiratory tract (preprint)

The visualization of viral pathogens in infected tissues is an invaluable tool to understand spatial virus distribution, localization, and cell tropism in vivo. Commonly, virus-infected tissues are analyzed using conventional immunohistochemistry in paraffin-embedded thin sections. Here, we demonstrate the utility of volumetric three-dimensional (3D) immunofluorescence imaging using tissue optical clearing and light sheet microscopy to investigate host-pathogen interactions of pandemic SARS-CoV-2 in ferrets at a mesoscopic scale. The superior spatial context of large, intact samples (> 150 mm3) allowed detailed quantification of interrelated parameters like focus-to-focus distance or SARS-CoV-2-infected area, facilitating an in-depth description of SARS-CoV-2 infection foci. Accordingly, we could confirm a preferential infection of the ferret upper respiratory tract by SARS-CoV-2 and emphasize a distinct focal infection pattern in nasal turbinates. Conclusively, we present a proof-of-concept study for investigating critically important respiratory pathogens in their spatial tissue morphology and demonstrate the first specific 3D visualization of SARS-CoV-2 infection.

Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis (preprint)

Timely access to accurate information is crucial during the COVID-19 pandemic. Prompted by key stakeholders' cautioning against an "infodemic", we study information sharing on Twitter from January through May 2020. We observe an overall surge in the volume of general as well as COVID-19-related tweets around peak lockdown in March/April 2020. With respect to engagement (retweets and likes), accounts related to healthcare, science, government and politics received by far the largest boosts, whereas accounts related to religion and sports saw a relative decrease in engagement. While the threat of an "infodemic" remains, our results show that social media also provide a platform for experts and public authorities to be widely heard during a global crisis.

COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter (preprint)

In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.