SEIR Model to address the impact of face masks amid COVID-19 pandemic.

Early in the pandemic of coronavirus disease 2019 (COVID-19), face masks were used extensively by the general public in several Asian countries. The lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Asian countries compared with Western countries suggested that the wider community use of face masks has the potential to decrease transmission of SARS-CoV-2. A risk assessment model named Susceptible, Exposed, Infectious, Recovered (SEIR) model is used to quantitatively evaluate the potential impact of community face masks on SARS-CoV-2 reproduction number (R0 ) and peak number of infectious persons. For a simulated population of one million, the model showed a reduction in R0 of 49% and 50% when 60% and 80% of the population wore masks, respectively. Moreover, we present a modified model that considers the effect of mask-wearing after community vaccination. Interestingly mask-wearing still provided a considerable benefit in lowering the number of infectious individuals. The results of this research are expected to help public health officials in making prompt decisions involving resource allocation and crafting legislation.

Post COVID-19 Vaccination-Associated Neurological Complications.

PURPOSE: Neurological sequelae after COVID-19 vaccination are rare. We investigated the possible pathogenesis behind the development of neurological complications within a short period after Saudi residents received a COVID-19 vaccine. PATIENTS AND METHODS: We evaluated 18 patients who recently received a COVID-19 vaccine (Comirnaty and Vaxzevria vaccines) and presented with neurological complications to the Saudi German Hospitals in Jeddah, Saudi Arabia. Neurologists assessed the patients' clinical presentation, radiological investigations, and laboratory findings. RESULTS: Three patients who received the first dose of the Vaxzevria vaccine experienced severe cerebral venous thrombosis, two of them were complicated by intracranial hemorrhage. Their laboratory investigations showed very high d-dimers and severe thrombocytopenia, which have been linked to higher mortality and poor outcome. Ischemic stroke occurred in eight cases (44.4%) with a predominance in older male patients. Three patients presented with seizures, two had optic neuritis. Guillain-Barré syndrome (GBS) and Miller Fisher syndrome (MFS) occurred in two male patients following vaccination with Comirnaty. CONCLUSION: Neurological complications after COVID-19 vaccinations are very rare, and only a few cases have been reported worldwide. The shared pathophysiological basis between COVID-19 viral infection and COVID-19 vaccines stands behind the very rare neurological complications resulting from the hypercoagulable state triggered by the general inflammatory condition. We suspect some differences in the pathogenesis of ischemic stroke caused by COVID-19 infection and COVID-19 vaccines, which render COVID-19 vaccine-associated ischemic stroke more responsive to treatment. To date, no definitive association between the vaccine and GBS has been proven by any strong evidence, but it has recently been added as a very rare side effect of the Janssen COVID-19 vaccine. No possible links of Miller Fisher syndrome to COVID-19 vaccines have been reported before the one reported in this study.

Public fear of COVID-19 vaccines in Iraqi Kurdistan region: a cross-sectional study

BackgroundVaccines remain one of the most effective methods to control infectious diseases;however, COVID-19 vaccines are challenging and novel. Vaccine support is still substantial in general, although vaccination fear has increased dramatically in recent decades. This is the first study aimed to determine the fear of the COVID-19 vaccination and the role of factors and reasons associated with fear in the Iraqi Kurdistan region.ResultsA total of 1188 participants responded to the questionnaire about their fears of the COVID-19 vaccine. The majority of participants had a medium level of fear (56.7%). Fear was significantly (p < 0.001) associated with major demographic characteristics, social media use (51.8%), and losing family members, while other variables (previous seasonal influenza vaccine, previous infection, chronic medical diseases) show no relationship. Fear of side effects such as blood clotting was reported by the majority (45.03%) and indicated positive relation (p < 0.016). On the other hand, a high proportion, 39.9% and 34.01%, were afraid of AstraZeneca and Pfizer (p < 0.001), respectively;however, only about 4.63% had fear of Sinopharm.ConclusionsThe fear of COVID-19 vaccination was widespread in the Iraqi Kurdistan region. In this way, fear was related to significant variables. To reduce the fear of vaccines and increase public acceptance, authorities and the Ministry of Health should initiate a public awareness campaign. As a result, the public health crisis will significantly improve.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

Psychological Strain of COVID19 and its Effect on Students Learning during Lockdown in Pakistan

People are struggling with a new corona virus emerged from China. This 21st century would be known by COVID19 in future. All the social activities and gathering have been banned due to this virus19. It destroyed the world badly. There are different sectors which have been collapsed and mainly are for example economy, finance, heath, and education sector. A group of people has died due to corona virus in the world. This study was conducted to examine the psychological strain of COVID19 and its effect on students learning during lockdown. It was quantitative and descriptive study. The survey method was used to identify psychological strain of covid19 and its effect on students learning. The population was included university students. Therefore, one hundred and eighty students were selected conveniently as sample of the study. A questionnaire was developed on 5 point likert scale to conduct study. It is concluded that a group of students said they felt pandemic infection in mind and due to this they felt depression. Social movement and gatherings were not possible due to corona virus. They felt anxiety and uncertainty. Results showed that psychological strain of covid19 significantly correlated with students learning. The structural equation modeling showed that the psychological strain of covid19 was correlated and affected students learning in uncertain situation. Therefore, students should spend quarantine time in remembrance of Allah Almighty and His beloved Holy Prophet Muhammad (PBUH) to overcome the stress. The students need to think positive in pandemic situation and concentrate on learning.

Acquired long QT syndrome due to antiemetics, COVID-19 and Blastocystis hominis induced exacerbation of congenital chloride losing diarrhoea.

Congenital chloride losing diarrhoea (CCLD) is a rare disease caused by mutations in an intestinal chloride/bicarbonate ion exchange channel. Few reports describe CCLD in adults and none has described the impact of a parasitic infection on CCLD. Severe diarrhoea may result in hypokalaemia with QT interval prolongation. Treatment with antiemetics may further increase the QT interval. To raise awareness of this preventable complication, we describe the course of a woman in her 20s with CCLD who developed COVID-19 and a Blastocystis hominis infestation. Treatment with antiemetics and hypokalaemia resulted in prolongation of the QT interval to 640 ms. While, the QT interval normalised with discontinuation of antiemetics and electrolyte replacement, patients with CCLD must take precautions to prevent gastrointestinal infections. Regardless, whenever patients with CCLD present to hospital, the authors recommend monitoring the QT interval and avoiding medications that predispose to torsade de pointes.

The Use of Proton Pump Inhibitors and COVID-19: A Systematic Review and Meta-Analysis.

COVID-19 has proved to be a serious, and consequential disease that has affected millions of people globally. Previously, the adverse effects of proton pump inhibitors (PPI) have been observed with increasing the risk of pneumonia and COVID-19. This meta-analysis aims to address the relationship between the use of PPI and the severity of COVID-19 infection. We conducted a systemic literature search from PUBMED, Science Direct, and Cinahl from December 2019 to January 2022. Published and unpublished randomized control trials and cohort studies were included. Review Manager was used for all statistical analyses. In total, 14 studies were included in this systemic review and meta-analysis. Outcomes of interest include: (1) susceptibility of COVID-19 infection and (2) severity of COVID-19 (defined as the composite of poor outcomes: ICU admission, need for oxygen therapy, need for a ventilator, or death), and (3) mortality due to COVID-19. PPI use was marginally associated with a nominal but statistically significant increase in the risk of COVID-19 infection (OR 1.05 [1.01, 1.09]; I2 97%, p = 0.007). PPI use also increased the risk of the composite poor outcome (OR 1.84 [1.71, 1.99]; I2 98%, p < 0.00001) and mortality (OR 1.12 [1.00, 1.25]; I2 84%, p = 0.05) in patients with COVID-19.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Understanding Education Difficulty During COVID-19 Lockdown: Reports on Malaysian University Students' Experience.

The COVID-19 pandemic has struck the world and forced countries to go into lockdown including education sector. Students have been staying in hostels or houses, unable to go to university campuses. This situation has left university administrators no choice, but to have an online learning channel. Malaysian universities in particular have gone through many challenges to bring their online learning system up and ready to resume education process. However, students have found themselves caught in this situation (pure online learning) with no plan or readiness. Literature reviews showed that students encountered some challenges that could not be easily resolved. This study explored the challenges encountered by students of a government-linked university. This university is one of the largest in Malaysia with over 10 campuses across the country. This study collected 284 valid answers. The findings show that respondents lacked full readiness in this situation physically, environmentally, and psychologically with some differences in perspectives according to their gender, age, and residing state. Respondents were concerned about the implications of lockdown on their performance. The findings of this study indicate that a sudden switch to a pure online alternative creates considerable challenges to students who have no plans to be physically apart from classes. The findings also indicate that the current blended learning process which uses online learning as a support mechanism for face-to-face learning has faced a considerable challenge to replace it, particularly with unprepared students.

Anxiety and depression symptoms among medical residents in KSA during the COVID-19 pandemic.

OBJECTIVES: Medical residents' direct contact with patients with COVID-19 places them at high risk of psychological disturbance. This study aimed to estimate the prevalence of depression and anxiety symptoms, and their relationship with the COVID-19 pandemic among medical residents in KSA. METHODS: A cross-sectional study was conducted between January and March of 2021. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale (GAD-7) were used to screen for depressive disorders and generalized anxiety disorder, respectively. RESULTS: A total of 533 medical residents participated in the study; 52% were men, and 58% were single. Most residents had direct contact with one or more patients with COVID-19. The prevalence of depression and anxiety symptoms was 65.8% and 58.3%, respectively. The study revealed that gender was a risk factor for diagnosis with COVID-19 among residents: male residents were diagnosed with COVID-19 to a greater extent than female residents. In addition, being a nonsmoker rather than a smoker was associated with a higher risk of COVID-19 diagnosis. A multivariate regression analysis revealed that gender (female) and residency level (R5) were independently associated with anxiety symptoms. Similarly, the independent correlates of depression symptoms were gender (female) and specialty (family medicine). CONCLUSION: A high prevalence of depression and anxiety symptoms was found among medical residents trained in KSA. The rates were significantly higher among female than male residents.

Impact of COVID-19 on Libyan Laboratory Specialists: A CrossSectional Survey

Background and objectives: Coronavirus disease 2019 (COVID-19) is a global health problem. Laboratory professionals are at a higher risk of contracting the disease during the COVID19 pandemic. This study was conducted to examine lab professionals' perceptions and satisfaction with organizational processes during the COVID-19 outbreak. Methods: This cross-sectional survey was carried out on 53 medical laboratory professionals working at laboratories in Tripoli (Libya) between November 2020 and January 2021. Data were collected via face-to-face questionnaire. Responses to questions were scored based on a five-point Likert scale (1=strongly disagree, 2= disagree, 3=neutral, 4=agree and 5=strongly agree). Data were presented as frequency and percentages using the Microsoft Excel 2016. Results: Most participants were female (81.2%) and with less than five years of work experience (39.6%). The majority of respondents (79.3%) had a bachelor’s degree. Most healthcare workers (75.5%) were unaware whether the lab would provide medical care if they were tested positive for COVID-19, while 13.2% of them perceived that they will get free medical care. Owing to social distancing, 20 subjects (37.7%) felt that their social activities have been altered during break time. Only 3.7% of the participants believed that their motivation level decreased due to the COVID-19 outbreak. Conclusion: The outcomes of this study provide laboratorians’ perspective in the COVID-19 crisis as well as specific lessons for future unpredicted crises. [ABSTRACT FROM AUTHOR] Copyright of Medical Laboratory Journal is the property of Golestan University of Medical Sciences, Deputy of Research & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fear of Nurses During COVID-19 Pandemic in Saudi Arabia: A Cross-Sectional Assessment.

Objective: We aimed to assess the level of fear among nurses in Saudi Arabia during the COVID-19 outbreak. Methods: A cross-sectional survey-based study was conducted from June to August 2020. All nurses currently working in public and private hospitals in Saudi Arabia during the COVID-19 pandemic were invited to complete an online survey. We used the 7-item unidimensional Fear of COVID-19 Scale (FCV-19S) to assess the level of fear of COVID-19. Multiple regression analysis was used to identify predictors associated with fear of COVID-19. Results: A total of 969 nurses participated in this study. The participants were relatively young with a mean age of 35.5 ± 10.46 years. About two-thirds of the participants were women (65.9%), married (57.2%), and were non-Saudi nationals (67%). The total mean score for the FCV-19S was 19.7 SD 7.03 (range 7-35), which is near the mid-point, indicating a moderate level of fear of COVID-19. Out of the eight variables measured in the analysis, three variables emerged as a significant predictor (i.e., gender, marital status, and age). A higher level of fear (FCV-19S) was associated with being a woman, married, and older age (p ≤ 0.05). Conclusion: This study demonstrated the level of fear of COVID-19 among nurses in Saudi Arabia. Overall, nurses in Saudi reported moderate levels of fear of COVID-19. Assessing the level of fear of nurses who work during the COVID-19 pandemic should be a priority to health care administrators to prevent mental health difficulties or psychological injury.

Coping with Anxiety During the COVID-19 Pandemic: A Case Study of Academics in the Muslim World.

The COVID-19 pandemic has been a global phenomenon defined by uncertainty, fear and grief which has resulted in record high levels of stress and anxiety in the first half of 2020. It also led to an increased interest in the study of the role of belief, religion, and spirituality as responses to coping with and responding to the pandemic throughout different societal domains. This study explores the impact of anxiety and stress caused by the pandemic on Muslim academics' subjective well-being. It also explores correlations between coping and spirituality by assessing Muslim academics' coping strategies in overcoming stress and anxiety. To this end, this study sampled 480 Muslim academics ages 25-60 years residing in Muslim countries. The findings show a negative yet significant correlation between anxiety and well-being while also showing a positive and significant correlation between coping strategies and subjective well-being. The research also points to the role of coping strategies in reducing anxiety and stress, the resulting improvements in well-being for Muslim academics, and the mediating effect of coping strategies between anxiety, stress, and well-being for Muslim academics.

Investigating Misinformation Dissemination on Social Media in Pakistan (preprint)

Fake news and misinformation are one of the most significant challenges brought about by advances in communication technologies. We chose to research the spread of fake news in Pakistan because of some unfortunate incidents that took place during 2020. These included the downplaying of the severity of the COVID-19 pandemic, and protests by right-wing political movements. We observed that fake news and misinformation contributed significantly to these events and especially affected low-literate and low-income populations. We conducted a cross-platform comparison of misinformation on WhatsApp, Twitter and YouTube with a primary focus on messages shared in public WhatsApp groups, and analysed the characteristics of misinformation, techniques used to make is believable, and how users respond to it. To the best of our knowledge, this is the first attempt to compare misinformation on all three platforms in Pakistan. Data collected over a span of eight months helped us identify fake news and misinformation related to politics, religion and health, among other categories. Common elements which were used by fake news creators in Pakistan to make false content seem believable included: appeals to emotion, conspiracy theories, political and religious polarization, incorrect facts and impersonation of credible sources.

A CNN Classification Model For Diagnosis Covid19 (preprint)

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

The first clusters of Middle East respiratory syndrome coronavirus in Oman: Time to act.

INTRODUCTION: Middle East respiratory syndrome coronavirus (MERS-CoV), is an emerging infectious disease of growing global importance. This review describes the latest MERS-CoV clusters and the first cases of nosocomial transmission within health care facilities in Oman. We have highlighted lessons learned and proposed steps to prevent healthcare-associated infections. METHODS: A descriptive analysis of MERS-CoV cases was conducted between January 23 and February 16, 2019. The data from officials and other published sources used. RESULTS: Thirteen laboratory-confirmed cases of MERS-CoV were reported from three simultaneous clusters from two governorates without an epidemiological link between the clusters. Two clusters were reported from North Al Batinah Governorate, with nine cases (69%) and 1 cluster from South Ash Sharqiyah Governorate with four cases (31%). In total, four deaths were reported (case fatality rate 31%). Four cases (31%) reported were household contacts from the first cluster, 3 (23%) were nosocomial transmission in health care facilities (two for first and one from the second cluster) and 7 (54%) were community-acquired cases. CONCLUSIONS: The first local clusters of MERS-CoV reported with evidence suggestive of healthcare and household-associated transmission. Early diagnosis and strict implementation of infection control measures remain fundamental in preventing and managing MERS-CoV infection.

A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19) (preprint)

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.