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Background Hepatitis B virus (HBV) infection is a major worldwide healthcare problem with subsequent serious complications including cirrhosis and hepatocellular carcinoma (HCC). Hence, taking cognizance of HBV impact is critical for future planning of its control and prevention. Objectives To assess the prevalence of HBV in Egypt, analyse the demographic characteristics of HBV-infected patients and examine the common routes of its transmission. Methods This is a cross-sectional study of data from the Egyptian Health Issues Survey (EHIS), which employed a nationally representative sample of 16,004 individuals. The survey participants were categorized into two groups: group A, HBV positive, and group B, HBV negative. Comparative analysis was performed to identify demographic features and define possible risk factors. Results The total number of participants included in the study was 16,004. The mean age (± SD) was 33.5 (± 12.4) years. The prevalence of HBV was 1.52%. Demographic analysis showed that HBV was more prevalent among males, married people, people with jobs and smokers (P = 0.0011, 0.002, < 0.001 and 0.0036) respectively. Employing an adjusted multivariate logistic regression model, we observed an increased likelihood of HBV infection in married adults who received cupping without blood and who did not know if they had schistosomiasis injection therapy. Conclusion The application of special screening programs to highly susceptible patients and treatment optimization is recommended for the elimination of HBV. EHIS indicates the likely success of the previous Egyptian control plan for viral hepatitis through reducing several risk factors.

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Objectives: During the COVID-19 pandemic, a quick and reliable phone-triage system is critical for early care and efficient distribution of hospital resources. The study aimed to assess the accuracy of the traditional phone-triage system and phone triage-driven deep learning model in the prediction of positive COVID-19 patients. Setting: This is a retrospective study conducted at the family medicine department, Cairo University. Methods: The study included a dataset of 943 suspected COVID-19 patients from the phone triage during the first wave of the pandemic. The accuracy of the phone triaging system was assessed. PCR-dependent and phone triage-driven deep learning model for automated classifications of natural human responses was conducted. Results: Based on the RT-PCR results, we found that myalgia, fever, and contact with a case with respiratory symptoms had the highest sensitivity among the symptoms/ risk factors that were asked during the phone calls (86.3%, 77.5%, and 75.1%, respectively). While immunodeficiency, smoking, and loss of smell or taste had the highest specificity (96.9%, 83.6%, and 74.0%, respectively). The positive predictive value (PPV) of phone triage was 48.4%. The classification accuracy achieved by the deep learning model was 66%, while the PPV was 70.5%. Conclusion: Phone triage and deep learning models are feasible and convenient tools for screening COVID-19 patients. Using the deep learning models for symptoms screening will help to provide the proper medical care as early as possible for those at a higher risk of developing severe illness paving the way for a more efficient allocation of the scanty health resources.