<abstract> <p>The novel coronavirus 2019 (2019-nCoV) is a public health emergency of international concern resulting in adverse psychological impacts during the epidemic.</p> <sec> <title>Methods</title> <p>From 1 to 10 April 2020, we conducted an online survey. The online survey collected information on demographic data, precautionary measures against (2019-nCoV), self-health evaluation, knowledge, and concerns about (2019-nCoV), and appraisal of crisis management. The psychological impact was assessed by the General Anxiety Disorder 7-item (GAD-7) scale. The designed questionnaire was answered by participants, and collected data were statistically analyzed.</p> </sec><sec> <title>Results</title> <p>This study included 1200 respondents. In total, 80% of respondents rated the psychological impact; 18% reported minimal anxiety; 34% reported mild anxiety, and 48% with moderate anxiety symptoms. A large proportion (74%) believed that they were very or somewhat likely to contract (2019-nCoV) while only (35%) believed they were unlikely to survive if they contracted the disease. (58%) of the respondents, practiced the precautionary measures directed against person-to-person droplet spread. Respondents with a moderate level of anxiety were most likely to take comprehensive precautionary measures against the infection. Also, older, female, more educated people as well as those who are more likely to contract the infection.</p> </sec><sec> <title>Conclusions</title> <p>During the outbreak, more than half of the respondents rated the psychological impact as moderate anxiety. Thus, the psychological needs during the outbreak should be addressed appropriately. Our results highlight the need to promote protective personal health practices to interrupt the transmission of the (2019-nCoV) in the community. Therefore, educational public programs about preventive measures should be targeted at the identified groups with low current uptake of precautions.</p> </sec></abstract>
Oral mucosa is constantly subjected to external and internal stimuli and therefore manifests a spectrum of diseases that range from developmental, reactive and inflammatory to neoplastic (Effiom et al., 2011). Reactive lesions of the oral cavity are non-neoplastic lesions that result from low-grade chronic irritation or trauma of the oral mucosa. (Farynowska et al., 2018) such as chewing, food impaction, calculus, iatrogenic injuries such as broken teeth, overhanging dental restorations and extended flanges of denture (Reddy et al., 2012). Irritational fibroma, pyogenic granuloma, peripheral giant cell granuloma, and peripheral ossifying fibroma are common oral cavity reactive lesions. Other reactive lesions of the oral cavity include epulis fissuratum, inflammatory papillary hyperplasia and inflammatory fibrous hyperplasia (Kadeh et al., 2015). A wide variety of reactive lesions can affect the oral mucosa in form of papillary and verrucous lesions as verruca vulgaris, condyloma acuminatum and Heck’s disease. They may appear histologically pronounced papillary epithelial hyperplasia, in addition to the cytological features of viral infection.(Moutasimet al., 2017). Reactive lesions of the oral cavity are commonly seen in the gingiva and their occurrence in other places of the oral cavity, such as the tongue, palate, cheek and floor of the mouth is less common (Effiom et al., 2011). The clinical appearance of Reactive lesions of the oral cavity is characterized by tissue growth, with fibrous or flaccid consistency, reddish or normal color, sessile or pedunculated, and can occur in multiple intraoral sites. Patients may have no symptoms; or symptoms ranging from mild pain to bleeding. Radiographic findings are commonly absent, however, in rare cases of large lesions, a localized alveolar bone resorption could be noticed.(Dutra et al., 2019) . They are relatively common peripheral lesions which present as a range of clinically similar lesions. Diagnosis can be challenging if dentists are unfamiliar with their clinical and pathological across various populations. (Soyele et al., 2019). Reactive lesions of the oral cavity had a high incidence among oral pathologies. The understanding of their clinical features helps to achieve a clearer clinical and etiological diagnosis, and the knowledge of factors related to their development. This may contribute to adequate treatment and positive prognosis. (Dutra et al., 2019). Various studies have reported differences in the type of reactive lesions, age distribution, gender, location, and clinical behavior of these lesions in different populations (Naderi et al., 2012). Therefore, the incidence and demographic characteristics of reactive lesions in our community should be investigated to develop a coherent oral health policy. In addition, such information could be useful for epidemiological and teaching purposes. (Alhindi et al., 2019).
<span lang="EN-US">Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25<sup>th</sup>, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21<sup>st</sup>). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.</span>
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