We aim to describe the prevalence of diabetic ketoacidosis (DKA) in individuals admitted to a single centre with COVID-19. We identified 218 individuals hospitalised with COVID-19, of these four fulfilled criteria for DKA (4/218, 1.8%). We conclude DKA is common and severe in individuals hospitalised with COVID-19.

A case is presented highlighting the emerging association of COVID-19 with pneumomediastinum, even in patients who have never received mechanical ventilation or positive airway pressure.

contributed equally to the manuscript in terms of conception of the work, acquisition of data drafting and approval of the version to be published. Douglas Fink provided the majority of analysis and interpretation of the data, and lead re-drafting of the manuscript. James Cai, Karim El-Shakankery and George Sismey all contributed to the work in terms of acquisition of the data, revising and approval of the version to be published. Ankur Gupta-Wright contributed analysis and interpretation of data, and revising and approval of the version to be published. Charlotte Tai contributed in terms of conception of the work, revising and approval of the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Infecting large portions of the global population, seasonal influenza is a major burden on societies around the globe. While the global source sink dynamics of the different seasonal influenza viruses have been studied intensively, its local spread remains less clear. In order to improve our understanding of how influenza is transmitted on a city scale, we collected an extremely densely sampled set of influenza sequences alongside patient metadata. To do so, we sequenced influenza viruses isolated from patients of two different hospitals, as well as private practitioners in Basel, Switzerland during the 2016/2017 influenza season. The genetic sequences reveal that repeated introductions into the city drove the influenza season. We then reconstruct how the effective reproduction number changed over the course of the season. While we did not find that transmission dynamics in Basel correlate with humidity or school closures, we did find some evidence that it may positively correlated with temperature. Alongside the genetic sequence data that allows us to see how individual cases are connected, we gathered patient information, such as the age or household status. Zooming into the local transmission outbreaks suggests that the elderly were to a large extent infected within their own transmission network. In the remaining transmission network, our analyses suggest that school-aged children likely play a more central role than pre-school aged children. These patterns will be valuable to plan interventions combating the spread of respiratory diseases within cities given that similar patterns are observed for other influenza seasons and cities.

Background Early COVID-19 diagnosis prior to laboratory testing results is crucial for infection control in hospitals. Models exist predicting COVID-19 diagnosis, but significant concerns exist regarding methodology and generalisability. Aim To generate the first COVID-19 diagnosis risk score for use at the time of hospital admission using the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Design A multivariable diagnostic prediction model for COVID-19 using the TRIPOD checklist applied to a large single-centre retrospective observational study of patients with suspected COVID-19. Methods 581 individuals were admitted with suspected COVID-19; the majority had laboratory-confirmed COVID-19 (420/581, 72.2%). Retrospective collection was performed of electronic clinical records and pathology data. Results The final multivariable model demonstrated AUC 0.8535 (95% confidence interval (0.8121–0.8950). The final model used 6 clinical variables that are routinely available in most low and high resource settings. Using a cut-off of 2, the derived risk score has a sensitivity of 78.1% and specificity of 86.8%. At COVID-19 prevalence of 10% the model has a negative predictive value (NPV) of 96.5%. Conclusions Our risk score is intended for diagnosis of COVID-19 in individuals admitted to hospital with suspected COVID-19. The score is the first developed for COVID-19 diagnosis using the TRIPOD checklist. It may be effective as a tool to rule out COVID-19 and function at different pandemic phases of variable COVID-19 prevalence. The simple score could be used by any healthcare worker to support hospital infection control prior to laboratory testing results.