Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and automated segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL techniques is presented. Lastly, the challenges faced in the automated detection of COVID-19 using DL techniques and directions for future research are discussed.

No abstract

No abstract

No abstract

No abstract

No abstract

Background: The coronavirus disease 2019 (COVID-19) outbreak is a global health problem. It is necessary to provide evidence on its unprecedented psychological effects to develop effective psychological interventions. The current study aims to determine the anxiety severity level, coping strategies, and influencing factors in response to the COVID-19 pandemic among people aged 15 years and above in Gonabad, Iran. Methods: We conducted a cross-sectional survey via online questionnaires between February and March 2020. We evaluated the anxiety severity levels and coping strategies using the Corona Disease Anxiety Scale (CDAS) and Coping Inventory for Stressful Situations–Short Form (CISS-SF), respectively. Multinomial and ordinal logistic regression models were used to identify the predictors of coping strategies and anxiety. Results: Totally, 500 people completed the questionnaires (response rate: 73%). Of them, 53.4% (95% confidence interval [CI]: 48.9%- 57.8%) suffered moderate to severe levels of anxiety. More than half of the respondents (52.0%; 95% CI: 47.5%-56.4%) utilized emotional-based or avoidant coping strategies. People with no academic education (odds ratio [OR]: 2.16; 95% CI: 1.41- 3.31) and without physical exercise (OR: 2.04; 95% CI: 1.22-3.33) preferred emotional-based coping instead of problem-based coping strategy. Female gender (OR: 1.60, 95%, CI: 1.13-2.28), underlying medical conditions (OR: 2.52, 95% CI: 1.65–3.87), and emotional-based coping (OR: 4.06, 95% CI: 2.76–5.99) were associated with higher severity levels of anxiety. Conclusion: The severity of anxiety during the COVID-19 pandemic was significant among participants. Further attention is needed to enhance the mental health of the vulnerable population during the COVID-19 pandemic. Our findings also identified some factors related to the severity level of anxiety related to COVID-19 that could help formulate better psychological interventions.

BackgroundSpecial incidents are harmful events that can result in people’s death or injury. Despite registering special incidents’ data in Iran, no study has yet been conducted to identify the types, rates, mortality and morbidity of such incidents and their associated factors. The present study was conducted to assess the epidemiology of incidents and their associated factors during 2014 in Iran.MethodsIn this cross-sectional study, all special incidents of 2014 were examined. Data were initially collected by universities of medical sciences nationwide and then sent to the Disaster and Emergency Management Center in the Ministry of Health and Medical Education. The collected data were analyzed in this study using statistical tests of Chi-square and Pearson’s correlation coefficient using SPSS ver. 14.5.ResultsOut of 6,892 special incidents that occurred during 2014 in Iran, 6,781 cases were included, of which, the most prevalent were traffic crashes (71%), carbon monoxide poisoning (14%), drowning (3.5%), and other cases (11.5%) (which included suspicious deaths, explosions, group poisoning, quarrels, fires, falls from height, and building collapses). The incidents led to 37,313 injuries and 3,259 deaths, of which 78% of injuries and 75% of deaths were due to road traffic incidents.ConclusionGiven to relationship between occurrence of the incidents and special holidays; such incidents can be reduced through preventive planning and education. We recommend annual monitoring of special incidents and further studies on the associated factors.