Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel virus that belongs to the coronaviruses and causes coronavirus disease 2019 (COVID-19). In this study, we explored the demographic details, clinical features, and routinely conducted laboratory investigations of patients with COVID-19 during the second and third waves of the pandemic to understand their possible diagnostic and prognostic values in Egypt. Methods In this retrospective cohort study, the demographic characteristics, detailed medical history, laboratory findings, and symptoms of all enrolled patients with SARS-CoV-2 were collected from the medical records of Beni Suef University Hospitals between December 15, 2020, and April 15, 2021. Results This retrospective study included 473 patients, almost all of whom were elderly. The median age of the patients was 48 years, and those with moderate and severe disease were older than those with mild infections. The proportion of females was higher (63.4%) than males (36.6%). Diabetes mellitus (DM) was the most common comorbidity (17.3%), and fever was the most typical manifestation of COVID-19 (62.6%). Those with severe disease showed a higher C-reactive protein level (CRP) than those with moderate (p-value 0.009) or mild (p-value 0.01) diseases. Serum ferritin levels were significantly higher in patients with severe disease than in those with moderate disease (p-value 0.018). In contrast, D-dimer and serum creatinine were normal and showed no significant difference in all comparisons (p-value overall 0.21). Conclusion This study observed several variations in COVID-19 patients’ characteristics. The new manifestations included skin rash, bone and low back pains, and rigors. In contrast to females, most males had moderate-to-severe illness. Old age and higher body mass index was associated with increasing severity. D-dimer and complete blood count were normal and could not identify potential COVID‐19 patients. Patients who had mild illness were still at risk of developing post-COVID complications.

In epidemic situations such as the novel coronavirus (COVID-19) pandemic, face masks have become an essential part of daily routine life. The face mask is considered as a protective and preventive essential of everyday life against the coronavirus. Many organizations using a fingerprint or card-based attendance system had to switch towards a face-based attendance system to avoid direct contact with the attendance system. However, face mask adaptation brought a new challenge to already existing commercial biometric facial recognition techniques in applications such as facial recognition access control and facial security checks at public places. In this paper, we present a methodology that can enhance existing facial recognition technology capabilities with masked faces. We used a supervised learning approach to recognize masked faces together with in-depth neural network-based facial features. A dataset of masked faces was collected to train the Support Vector Machine classifier on state-of-the-art Facial Recognition Feature vector. Our proposed methodology gives recognition accuracy of up to 97% with masked faces. It performs better than exiting devices not trained to handle masked faces.

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others, are dependent on daily wages in the informal labour markets, or that many social gatherings in these countries are, in fact, queues for food and medication. Such omissions highlight the real dangers inherent to organisations adopting a narrowly epidemiological approach in a region in which the trajectory of the pandemic is so strikingly shaped by the social and political determinants of health.I declare no competing interests.

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