Background There are data on the safety of cancer surgery and the efficacy of preventive strategies on the prevention of postoperative symptomatic COVID-19 in these patients. But there is little such data for any elective surgery. The main objectives of this study were to examine the safety of bariatric surgery (BS) during the coronavirus disease 2019 (COVID-19) pandemic and to determine the efficacy of perioperative COVID-19 protective strategies on postoperative symptomatic COVID-19 rates. Methods We conducted an international cohort study to determine all-cause and COVID-19-specific 30-day morbidity and mortality of BS performed between 01/05/2020 and 31/10/2020. Results Four hundred ninety-nine surgeons from 185 centres in 42 countries provided data on 7704 patients. Elective primary BS (n = 7084) was associated with a 30-day morbidity of 6.76% (n = 479) and a 30-day mortality of 0.14% (n = 10). Emergency BS, revisional BS, insulin-treated type 2 diabetes, and untreated obstructive sleep apnoea were associated with increased complications on multivariable analysis. Forty-three patients developed symptomatic COVID-19 postoperatively, with a higher risk in non-whites. Preoperative self-isolation, preoperative testing for SARS-CoV-2, and surgery in institutions not concurrently treating COVID-19 patients did not reduce the incidence of postoperative COVID-19. Postoperative symptomatic COVID-19 was more likely if the surgery was performed during a COVID-19 peak in that country. Conclusions BS can be performed safely during the COVID-19 pandemic with appropriate perioperative protocols. There was no relationship between preoperative testing for COVID-19 and self-isolation with symptomatic postoperative COVID-19. The risk of postoperative COVID-19 risk was greater in non-whites or if BS was performed during a local peak.
Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.
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