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SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have been many applications of SEIR analyzing the spread of COVID to provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify the population, regardless of different demographic features and activities related to the spread of the disease. This paper provides a comprehensive SEIR model to enhance the prediction quality and effectiveness of intervention strategies. The new SEIR model estimates the exposed population via a new approach involving health conditions (sensitivity to disease) and social activity level (contact rate). To validate our model, we compare the estimated infection cases via our model with actual confirmed cases from CDC and the classic SEIR model. We also consider various protocols and strategies to utilize our modified SEIR model on many simulations and evaluate their effectiveness. © 2022 IEEE.
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Introduction: Pediatric COVID-19 cases are typically known to be mildly symptomatic and show a good prognosis. However, more severe condition termed Multisystem inflammatory syndrome (MIS-C) is encountered in children. This research aimed to evaluate the differences between MIS-C and non-MIS-C (children who were infected with SARS-CoV-2 but did not develop MIS-C) patients according to demographics, comorbidities, and symptoms conditions, as well as clinical, laboratory, radiological findings, treatment, and prognosis. Materials and Methods: This systematic review and meta-analysis were performed in accordance with PRISMA guidelines using electronic databases of PubMed, Scopus, Science-Direct, and LitCovid including articles on observational studies comparing the MIS-C and non-MIS-C cases published between 01 January 2020-15 January 2021. Results: Seventeen articles meeting the criteria were included. No difference was found in terms of gender and age from the demographic characteristics of the MIS-C and non-MIS-C groups. Black race and clinical findings such as fever, rash, fatigue, loss of appetite, vomiting and diarrhea, and laboratory findings CRP and ferritin were found to be higher in the MISC group compared to the nonMISC group (p<0.05). Cardiac complications, use of some medical treatments (steroids, IVIG, inotropic therapy), and need for intensive care were also higher (p< 0.05). Conversely, the presence of comorbidity, presence of rhinoirhea, hemoglobin, lymphocyte, and platelet values were higher in the non-MIS-C group (p< 0.05). Conclusion: Evaluation of MIS-C and non-MIS-C patients for various characteristics revealed differences that will guide the diagnosis of and approach to MIS-C cases.
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Objectives: To determine the predictive association between fear of COVID-19 and emotional distress (depression, anxiety, and stress) in frontline and non-frontline nurses. To explore the mediating role of socio-demographic features. Methods: Correlational cross-sectional research design was implied. A total of 500 on-duty male and female, frontline and non-frontline, nurses were included from five major hospitals in Gujrat (Aziz Bhatti Shaheed Hospital, City Hospital, Doctors Hospital, Akram Hospital, and Gujrat Hospital). Fear of COVID-19 scale and the Urdu version of depression, anxiety, and stress scale - 21 (DASS-21) were used to measure variables of interest. Descriptive statistics, structural equation modeling (SEM), linear regression, and t-test were carried out using Statistical Package for Social Sciences (SPSS) 21. Result: Structural equation modeling (SEM) revealed a significant predictive link between fear of COVID-19 and depression, anxiety, and stress (goodness of model fit; NFI = 0.93, GFI = 0.914, AGFI = 0.93, CFI = 0.936, and IFI = 0.936). Furthermore, a significant mediating effect of certain demographic features was discovered by SEM (CMIN/DF = 1.11, NFI = 0.94, TLI = 0.98, GFI = 0.08, AGFI = 0.93, RMSEA = 0.029, CFI = 0.99, and IFI = 0.99). Results of linear regression analysis also revealed a momentous predictive association between fear of COVID-19 and emotional distress (R = 0.860). In comparative analysis, the results of t-test explored the statistical significant difference in fear of COVID-19 and emotional distress between frontline (mean = 25.775, 36.147 and SD = 1.75, 2.23) and non-frontline nurses (mean = 21.702, 27.353 and SD = 4.607, 10.212), with t (130) =7.111, 6.92. Conclusion: Managing the mediating effect of demographic characteristics and reducing the fear of COVID-19 can help nurses to overcome emotional distress, such as depression, anxiety, and stress. Further, this will increase the productivity among nurses.
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BACKGROUND: This study presents the demographic, epidemiological, and clinical characteristics of Coronavirus Disease 2019 (COVID-19) in Saudi Arabia (KSA). It identifies the important predictors of the disease prognosis. METHODS: The study reviewed and analysed a sample of 307,010 confirmed symptomatic COVID-19 cases, between March and August 2020, available in the health electronic surveillance system (HESN) of the Ministry of Health of KSA. Descriptive and univariate analyses were conducted. RESULTS: The overall estimated prevalence of symptomatic COVID-19 cases in KSA between March and August 2020 was 6.1% . The estimated incidence proportion was 879.7 per 100,000 population. The overall case fatality ratio was 2.0%. Males represented 63.9% , with a mean age of 35.1 ± 16.6 years. Young adults (16-39 years) were the most affected ages (53.3%). Fever (90.5%) with a mean body temperature of 37.4 ± 2.0 Celsius, cough (90%), and sore throat (77.4%) were the most prevalent symptoms. A history of contact with a confirmed COVID-19 case was reported in 98.8% of patients.Males (2.1%) and elderly cases aged 65-99 years (25.6%) had the highest association with mortality (p < .001). Among the clinical characteristics investigated, low oxygen saturation (SpO2 ≤ 93%) had the highest association with hospital admission (50.8%) and mortality (19.1%) (p < .001). Cases with cardiovascular diseases (28.6%) and malignancy (28%) demonstrated the highest associations with mortality compared to other underlying diseases (p < .001). CONCLUSIONS: In KSA, the prevalent symptoms of COVID-19 are fever, cough, and sore throat. Makkah and Almadinah regions are significantly associated with highest burden of mortality. The low level of oxygen saturation, high fever, old age, and underlying cardiovascular disease are the most important predictors for prognosis.
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The fast-growing outbreak of the 2019 novel coronavirus (2019-nCoV), which originated from Wuhan locating in central China at the end of 2019, spread to multiple cities in merely a month. Although the mortality of this disease was lower than that of SARS, the incredible contagiousness was much higher than SRAS-CoV. Because of the tremendous clout of 2019-nCoV, it is essential to hold more details about it and monitor its future evolution. This mini review consequently summarizes the key elements of epidemiology features, providing updated relevant findings and novel insights related to 2019-nCoV.