Suchergebnisse

The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.

Current evidence describing antimicrobial resistance (AMR) in the context of the Syrian conflict is of poor quality and sparse in nature. This paper explores and reports the major drivers of AMR that were present in Syria pre-conflict and those that have emerged since its onset in March 2011. Drivers that existed before the conflict included a lack of enforcement of existing legislation to regulate over-the-counter antibiotics and notification of communicable diseases. This contributed to a number of drivers of AMR after the onset of conflict, and these were also compounded by the exodus of trained staff, the increase in overcrowding and unsanitary conditions, the increase in injuries, and economic sanctions limiting the availability of required laboratory medical materials and equipment. Addressing AMR in this context requires pragmatic, multifaceted action at the local, regional, and international levels to detect and manage potentially high rates of multidrug-resistant infections. Priorities are (1) the development of a competent surveillance system for hospital-acquired infections, (2) antimicrobial stewardship, and (3) the creation of cost-effective and implementable infection control policies. However, it is only by addressing the conflict and immediate cessation of the targeting of health facilities that the rehabilitation of the health system, which is key to addressing AMR in this context, can progress.

Current evidence describing antimicrobial resistance (AMR) in the context of the Syrian conflict is of poor quality and sparse in nature. This paper explores and reports the major drivers of AMR that were present in Syria pre-conflict and those that have emerged since its onset in March 2011. Drivers that existed before the conflict included a lack of enforcement of existing legislation to regulate over-the-counter antibiotics and notification of communicable diseases. This contributed to a number of drivers of AMR after the onset of conflict, and these were also compounded by the exodus of trained staff, the increase in overcrowding and unsanitary conditions, the increase in injuries, and economic sanctions limiting the availability of required laboratory medical materials and equipment. Addressing AMR in this context requires pragmatic, multifaceted action at the local, regional, and international levels to detect and manage potentially high rates of multidrug-resistant infections. Priorities are (1) the development of a competent surveillance system for hospital-acquired infections, (2) antimicrobial stewardship, and (3) the creation of cost-effective and implementable infection control policies. However, it is only by addressing the conflict and immediate cessation of the targeting of health facilities that the rehabilitation of the health system, which is key to addressing AMR in this context, can progress.