In the article a virus transmission model is constructed on a simplified social network. The social network consists of more than 2 million nodes, each representing an inhabitant of Slovenia. The nodes are organised and interconnected according to the real household and elderly-care center distribution, while their connections outside these clusters are semirandomly distributed and undirected. The virus spread model is coupled to the disease progression model. The ensemble approach with the perturbed transmission and disease parameters is used to quantify the ensemble spread, a proxy for the forecast uncertainty. The presented ongoing forecasts of COVID-19 epidemic in Slovenia are compared with the collected Slovenian data. Results show that at the end of the first epidemic wave, the infection was twice more likely to transmit within households/elderly care centers than outside them. We use an ensemble of simulations (N = 1000) and data assimilation approach to estimate the COVID-19 forecast uncertainty and to inversely obtain posterior distributions of model parameters. We found that in the uncontrolled epidemic, the intrinsic uncertainty mostly originates from the uncertainty of the virus biology, i.e. its reproduction number. In the controlled epidemic with low ratio of infected population, the randomness of the social network becomes the major source of forecast uncertainty, particularly for the short-range forecasts. Virus transmission models with accurate social network models are thus essential for improving epidemics forecasting.

Acute kidney injury after cardiac surgery with cardiopulmonary bypass is a common and serious complication and it is associated with increased morbidity and mortality. Diagnosis of acute kidney injury is based on the serum creatinine levels which rise several hours to days after the initial injury. Thus, novel biomarkers that will enable faster diagnosis are needed in clinical practice. There are numerous urine and serum proteins that indicate kidney injury and are under extensive research. Despite promising basic research results and assembled data, which indicate superiority of some biomarkers to creatinine, we are still awaiting clinical application.

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Objectives Data on the long-term survival outcome of patients with missed upper gastrointestinal cancers (MUGC) is lacking. Retrospective studies have found no difference in 1- and 2-year survival among patients with missed gastric and oesophageal cancers; we thus aimed to assess 3-year survival of patients with MUGC at oesophagogastroduodenoscopy. Methods This was a retrospective cohort study conducted at a single tertiary endoscopy centre. All oesophagogastroduodenoscopies performed between January 2007 and December 2015 were included in the study. The endoscopy database was cross-matched with the Slovenian Cancer Registry database. Missed cancers were defined as those diagnosed within 36 months after a negative oesophagogastroduodenoscopy. Results During the study period, 29 617 oesophagogastroduodenoscopies were performed. In total, 422 upper gastrointestinal cancers were diagnosed and the rate of missed gastric cancers was 7.3% (95% CI, 4.9–10.6%) (26/354), and 4.4% (95% CI, 0.9–12.4%) for oesophageal cancers (3/68). Three-year survival of patients with MUGC was shorter than that of those with non-MUGC, being 12% (95% CI, 1–25%) vs. 31% (95% CI, 26–36%) (P = 0.043) for gastric and 0 vs. 9% (95% CI, 1–17%) (P = 0.121) for oesophageal cancer. Conclusion Missed gastric cancer during oesophagogastroduodenoscopy may be associated with shorter 3-year survival compared to patients whose gastric cancer was diagnosed at index oesophagogastroduodenoscopy.

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