Suchergebnisse

Aim. Contact networks play a crucial role in infectious disease propagation and position in the network mediate risk of acquiring or sending infections. We studied the spread of hospital-associated infections through computer simulations and validated our 'computer assisted' risk assessment with 'human' risk assessment in a prospective study.Concept. We collected time-varying structure of contacts and covariates reconstructed from Polish Hospitals:1. The organisational structure is mapped by a set of questionnaires, CAD maps integration, functional paths annotation and local vision. It is done mostly by surveys within medical staff through an interactive web application.2. The Cohabitation layer processes data from the registry of patient admissions and discharges from each hospital unit (wards, clinics, etc.) and medical shift register. With simulated infection paths, we were able to compute network centrality measures for patients. We obtained the risk of getting infected, based on the patient's incoming connections, and the risk of spreading infections resulting from outgoing connections. We compare various standard centrality measures – position of patients and staff in contact networks ('computer assisted' risk assessment) of both contacts and paths networks, with a predictor of 'human' risk perception (based on 190 patients).Results. We showed that the best predictor of HAI risk is Adjusted Rage Rank on paths (r= 0.42, p < 0.01). However, surprisingly good predictive power in risk assessment was found in the betweenness centrality of the underlying network of contacts (r = 0.30, p < 0.01).Conclusion: We conclude that epidemiology of a given pathogen in a given place and time could be explained only with the contact network only to a large extent. However, further possibility of the collection, processing and storage of the data on individual persons, translated to mathematical modelling could lead in future to satisfactory improvement in risk assessment.

Introduction. This study examines the relationship between social values and the control of the COVID-19 pandemic in selected European countries during the early months of 2020.
Methods. Utilizing epidemiological data, including starting and controlled reproduction rates (R(t)), and social values' dimensions based on Schwartz's framework, we uncover significant associations by linear regressions.
Results. Our findings reveal that highly hierarchical societies with a strong focus on maximising individual utility (affective autonomy) are less successful in controlling the spread of the virus. Conversely, societies with interconnected (embedded) structures appear to be more effective in disease control. Our discussion acknowledges the complexity of distinguishing between natural disease patterns and control efforts. Interactive dashboard is available: https://infodemia-koronawirusa.shinyapps.io/culture/
Conclusions. This study underscores the substantial influence of social values on disease control in Europe. It emphasises the need to consider social contexts when evaluating the effectiveness of mitigation strategies on an international scale. While recognising the potential role of confounding variables such as healthcare capacity, our research suggests that analysing outbreak dynamics across countries can provide insights into not only why some nations fare better but also how to combat future disease spread effectively.