Jellyfish risk communications: The effect on risk perception, travel intentions and behaviour, and beach tourism destinations

Jellyfish hazards at 3S destinations are underrepresented in tourism research. Using a novel conceptual model based on risk perception and destination image theories, we used an experimental setting to examine whether different types of jellyfish risk messages influenced people's travel intentions and behaviours. In addition, the study tested the influence of worry and culture. We sampled 415 prospective visitors to two of the world's most successful beach tourism destinations, the Costa Brava coastline of Spain and the Great Barrier Reef region of northern Australia, both adversely affected by the presence of jellyfish. At these unique destinations, contact with jellyfish can be painful and deadly. Early in the Covid-19 pandemic, fictitious vignettes were posted on an internet Travel Forum containing two different jellyfish risk messages, one informal and the other official. Participants' responses to these communications were tested. We found that risk messages influenced destination image but not travel intention. People from risk-averse Germanic European countries were more inclined to alter their behaviour by avoiding the water than other cultures. These findings add to the body of knowledge about the relationship between risk communications, risk perceptions and destination image. This study suggests that wildlife-associated risk communications can influence people's risk perceptions, but not sufficiently to change their travel plans. This knowledge is important in policy-making and managing responses to risk at tourism destinations. It is also important in building visitor trust and confidence, whereby tourists know that their safety and enjoyment are valued and are paramount to the destination.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Harmonized D-dimer levels upon admission for prognosis of COVID-19 severity: Results from a Spanish multicenter registry (BIOCOVID-Spain study).

Coagulopathy is a key feature of COVID-19 and D-dimer has been reported as a predictor of severity. However, because D-dimer test results vary considerably among assays, resolving harmonization issues is fundamental to translate findings into clinical practice. In this retrospective multicenter study (BIOCOVID study), we aimed to analyze the value of harmonized D-dimer levels upon admission for the prediction of in-hospital mortality in COVID-19 patients. All-cause in-hospital mortality was defined as endpoint. For harmonization of D-dimer levels, we designed a model based on the transformation of method-specific regression lines to a reference regression line. The ability of D-dimer for prediction of death was explored by receiver operating characteristic curves analysis and the association with the endpoint by Cox regression analysis. Study population included 2663 patients. In-hospital mortality rate was 14.3%. Harmonized D-dimer upon admission yielded an area under the curve of 0.66, with an optimal cut-off value of 0.945 mg/L FEU. Patients with harmonized D-dimer ≥ 0.945 mg/L FEU had a higher mortality rate (22.4% vs. 9.2%; p < 0.001). D-dimer was an independent predictor of in-hospital mortality, with an adjusted hazard ratio of 1.709. This is the first study in which a harmonization approach was performed to assure comparability of D-dimer levels measured by different assays. Elevated D-dimer levels upon admission were associated with a greater risk of in-hospital mortality among COVID-19 patients, but had limited performance as prognostic test.