Background: In December 2019, a cluster of patients with unexplained viral pneumonia was identified in Wuhan, China. Since March 11^th 2020 the WHO declared COVID 19 as a pandemic with rising number of cases all over the world. Aim of the work: The aim of the study was to measure the percentages of possible, probable and provisionally excluded cases among the first 500 attendants of the triage of Ain Shams University Hospital and describe their epidemiological and clinical characteristics. Patients and Methods: This was a retrospective descriptive case series study including the first 500 patients attending the triage of Ain Shams University Hospitals from March 29th to May 31st. A constructed questionnaire in the form of a scoring system was used and data was collected through interviewing the patients after appropriate consent. Results: As regard the scoring system, 72.2% of patients had new onset of cough or old worsened cough in the previous 3 days, 59.2% had sore throat and 59% had dyspnea. Out of the 500 cases 33.2% were probable, 38.2% were possible and 28.2% were provisionally excluded. Conclusion: COVID-19 pneumonia usually occurred at an age younger than 47 years and it was more predominant in the male gender. The most common initial clinical presentations were new dry cough or chronic cough with worsening over the last 3 days, sore throat and/or runny nose and fever. Thirty-eight percent were classified as possible COVID-19 cases, and 33% were classified as probable.

Artificial intelligence (AI) based triage and diagnostic systems are increasingly being used in healthcare. Although these online tools can improve patient care, their reliability and accuracy remain variable. We hypothesized that an artificial intelligence (AI) powered triage and diagnostic system (MayaMD) would compare favorably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by an AI system and individual Indian healthcare practitioners (HCPs) to draw comparison for accuracy and safety. The same cases were validated with the help of consensus received from an expert panel of 3 doctors. These cases in the form of clinical vignettes were provided by an expert medical team. Overall, the study showed that the MayaMD AI based platform for virtual triage was able to recommend the most appropriate triage ensuring patient safety. In fact, the accuracy of triage recommendation by MayaMD was significantly better than that provided by individual HCPs (74% vs. 91.67%, p=0.04) with consensus being used as standard.

Key business objectives for digital infrastructure cloud adoption are often framed in terms of reducing cost, improving fault tolerance and resilience, simplifying scale, and enabling innovation. Given the critical nature of the financial sector, however, where timeliness and price can significantly determine an outcome, cloud migration in delivery environments demands greater throughput on the critical path and, in many enterprise-scale settings, forgoes hybrid complexity and multi-cloud risks. Nevertheless, slack in system designs does exist; financial institutions enable market functionality—trading, clearing/best execution—despite potentially being able to meet such sets with lower service levels than other verticals. A cloud multi-account structure for sensitive data, for example, naturally limits exposure when combined with observed risk. Fulfilling predictions of elasticity during periods of high demand usually requires support from a dedicated environment (or environments) located nearer to the operations. Components can consequently be allocated on a per-account basis or maintained as shared sink systems to which the dedicated streams write. The automation code can similarly be targeted for dedicated accounts, avoiding the resource constraints that beset such operations during industry events like emergency triage/contact desking.