Poor Hemorrhagic Stroke Outcomes During the COVID-19 Pandemic Are Driven by Socioeconomic Disparities (preprint)

Background Nationally representative data demonstrating the impact of the COVID-19 pandemic on hemorrhagic stroke outcomes are lacking. Methods In this pooled cross-sectional analysis, we used the National Inpatient Sample (2016-2020) to identify adults (>=18 years) with primary intracerebral hemorrhage (ICH) or subarachnoid hemorrhage (SAH). We fit segmented logistic regression models to evaluate the differences in the rates of in-hospital outcomes (in-hospital mortality, home discharge, and receiving neurosurgical procedures) between the pre-pandemic (January 2016-February 2020) and pandemic periods (March 2020-December 2020). We used multivariable logistic regression models to evaluate the differences in mortality between patients admitted from April to December 2020, with and without COVID-19, and those admitted during a similar period in 2019. Stratified analyses were conducted among patients residing in low and high-income zip codes and among patients with extreme loss of function (E-LoF) and those with minor to major loss of function (MM-LoF). Results Overall, 309,965 ICH patients (mean age [SD]: 68[14.8], 47% female, 56% low-income) and 112,210 SAH patients (mean age [SD]: 60.2[15.4], 62% female, 55% low-income) were analyzed. Pre-pandemic, ICH mortality was decreasing by {approx}1% per month (adjusted odds ratio, 95% confidence interval: 0.99, 0.99-1.00). However, during the pandemic, the overall ICH mortality rate increased by {approx}2% per month (1.02, 1.00-1.02) and {approx}4% per month among low-income patients (1.04, 1.01-1.07). However, there was no change in trend among high-income ICH patients during the pandemic (1.00, 0.97-1.03). Patients with comorbid COVID-19 in 2020 had significantly higher odds of mortality compared to the 2019 comparison cohort, overall (ICH: 1.83, 1.33-2.51; SAH: 2.76, 1.68-4.54), and among patients with MM-LoF (ICH: 2.15, 1.12-4.16; SAH: 5.77, 1.57-21.17). However, patients with E-LoF and comorbid COVID-19 had similar mortality rates with the 2019 cohort. Conclusion Sustained efforts are needed to address socioeconomic disparities in healthcare access, quality, and outcomes during public health emergencies.

Fiscal Policies and Post-COVID-19 Development Challenges: An Overview

The COVID-19 pandemic has created an unprecedented crisis. The pandemic poses a significant threat to human security and existing developmental challenges, compelling emergency spending on saving lives and securing livelihoods. Despite being in the ongoing traumatic phases of the global pandemic, the world is further expected to face undue developmental challenges due to the fourth industrial revolution and climate change. In a massive effort to save the global economies and protecting livelihoods, national governments had been obliged to announce stimulus (fiscal) packages and create local and regional funds to boost up domestic production and ensure food, water, and energy security. This chapter intends to provide a broader overview of the implications of fiscal stimulus toward sustainable recovery and address post-COVID-19 developmental challenges in a cross-country setting. Although expectantly incentivizing through fiscal policies will mobilize food security, public health, climate security and environment, migrant worker and urban resilience, gender, education, and facilitate attainment of the respective sustainable development goals (SDGs);sustainable recovery could largely vary based upon national resilience and the extant developmental framework. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.

VECTOR AUTOREGRESSIVE INTEGRATING MOVING AVERAGE (VARIMA) MODEL OF COVID-19 PANDEMIC AND OIL PRICE

Purpose: A coronavirus associated with severe respiratory syndrome has created Coronavirus Disease 2019 (COVID-19), a highly contagious illness that affects the entire world population. On the other hand, COVID-19 is having a direct impact on human life because of its proliferation. So, the study's goal is to forecast and analyze the impact of the COVID-19 pandemic and the oil price utilizing multiple time series analysis methods (VARIMA model). Theoretical framework: Recent literature has reported that the multivariate time series is robust model for forecasting and analyzing dynamic relationship between series, while the univariate ARIMA model has been generalized to include vector variables, that is an extension of its capabilities. The VAR (p) model analyzes the interdependence between two or more series but does not take into account the impact of shocks at various time variable delays. Design/methodology/approach: This study uses VARMA (p, q) model which links a set of variables to their prior iterations as well as those of other variables and shocks to those same variables. Sample data concerning the COVID-19 pandemic and oil price was globally provided. It contains daily observations of them variables for the years 2020-2022. Findings: The best model is VARIMA (2,1,2), and the results shown that the oil price is not only influenced by itself but also influenced by the Covid-19 pandemic. Moreover, the standard error grows over time of the forecast. Research, Practical & Social implications: The best model is sound for short-term forecasting but unstable for long-term forecasting. Future researchers can integrate factors across areas. Include tourism demand and industry variables in modeling. Originality/value: Collecting COVID-19 pandemic data and oil price series in a modern model that is a multivariate time series model with a high predicted level of model accuracy between these variables in order to predict and analyze the effects between them series and estimate the interaction between these two series with the most recent data is the value of this study, and then offers merchants the chance to comprehend the forecasting of oil price throughout the covid-19 effects as well as the associated risks. © 2022 AOS-Estratagia and Inovacao. All rights reserved.

Evaluating the Performance of a Commercially Available AI Algorithm for Automated Detection of Pulmonary Embolism on CECT and CTPA of COVID-19 Patients

Objective To investigate the performance of a commercially available artificial intelligence (AI) algorithm for detection of pulmonary embolism (PE) on contrast-enhanced CTs in patients hospitalized for COVID-19. Patients & Methods Retrospective analysis was performed of all contrast-enhanced chest CTs on patients admitted for COVID-19 between March 2020 and December 2021. Based on the original radiology reports, all PE-positive exams were included (n=527). Using a reversed flow single gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative exams (n=977) was included. Pulmonary parenchymal disease severity was assessed for all included studies using a semi-quantitative system, the Total Severity Score (TSS). All included CTs were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by three blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and excluded. The sensitivity and specificity of AI was 93.2%;(95% confidence interval [CI] 90.6-95.2%), and 99.6%;(95% CI 98.9-99.9%), respectively. AI's accuracy for all TSS groups (mild, moderate, severe) was high (98.4%, 96.7%, and 97.2%, respectively). AI was more accurate in PE detection on CTPAs vs CECTs (P < .001), with optimal HU of 362 (P=.048). Conclusion The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast enhanced CTs in COVID-19 patients regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Evaluating the Performance of a Commercially Available Artificial Intelligence Algorithm for Automated Detection of Pulmonary Embolism on Contrast-Enhanced Computed Tomography and Computed Tomography Pulmonary Angiography in Patients With Coronavirus Disease 2019.

Objective: To investigate the performance of a commercially available artificial intelligence (AI) algorithm for the detection of pulmonary embolism (PE) on contrast-enhanced computed tomography (CT) scans in patients hospitalized for coronavirus disease 2019 (COVID-19). Patients and Methods: Retrospective analysis was performed of all contrast-enhanced chest CT scans of patients admitted for COVID-19 between March 1, 2020 and December 31, 2021. Based on the original radiology reports, all PE-positive examinations were included (n=527). Using a reversed-flow single-gate diagnostic accuracy case-control model, a randomly selected cohort of PE-negative examinations (n=977) was included. Pulmonary parenchymal disease severity was assessed for all the included studies using a semiquantitative system, the total severity score. All included CT scans were sent for interpretation by the commercially available AI algorithm, Aidoc. Discrepancies between AI and original radiology reports were resolved by 3 blinded radiologists, who rendered a final determination of indeterminate, positive, or negative. Results: A total of 78 studies were found to be discrepant, of which 13 (16.6%) were deemed indeterminate by readers and were excluded. The sensitivity and specificity of AI were 93.2% (95% CI, 90.6%-95.2%) and 99.6% (95% CI, 98.9%-99.9%), respectively. The accuracy of AI for all total severity score groups (mild, moderate, and severe) was high (98.4%, 96.7%, and 97.2%, respectively). Artificial intelligence was more accurate in PE detection on CT pulmonary angiography scans than on contrast-enhanced CT scans (P<.001), with an optimal Hounsfield unit of 362 (P=.048). Conclusion: The AI algorithm demonstrated high sensitivity, specificity, and accuracy for PE on contrast-enhanced CT scans in patients with COVID-19 regardless of parenchymal disease. Accuracy was significantly affected by the mean attenuation of the pulmonary vasculature. How this affects the legitimacy of the binary outcomes reported by AI is not yet known.

Telemedicine in Spine Patients: Utilization and Satisfaction Remain High Even After Easing of COVID-19 Lockdown Restrictions.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: The objectives of the present study were to 1) define telemedicine utilization rates during and after the initial height of the COVID-19 lockdown period and 2) determine patient satisfaction with telemedicine during and after the initial height of the COVID-19 lockdown period for spine patients at an orthopedic specialty hospital. SUMMARY OF BACKGROUND DATA: Previous studies have shown high patient satisfaction with telemedicine during the initial height of the COVID-19 pandemic. However, there exists limited data about spine telemedicine utilization and patient satisfaction after the reopening of in-person office visits and easing of restrictions on elective surgical care. METHODS: All patients who had an in-person or telemedicine visit at an urban tertiary specialty hospital from April 1, 2020 to April 15, 2021 were identified. Rates of overall telemedicine utilization over time were delineated. Patient satisfaction with telemedicine, as assessed through a series of questionnaires, was also evaluated over time. RESULTS: Overall, 60,368 patients were identified. Of these, 19,568 patients (32.4%) had a telemedicine visit. During the peak initial coronavirus lockdown period, rate of overall telemedicine utilization on average was greater than 90%. After the peak period, the rate of overall telemedicine utilization on average was at approximately 29% of all visits per month. The percentage of patients who would have been definitely comfortable if the telemedicine visit had been in-person increased over the entire study period (P<0.001). Despite this, patient satisfaction based on survey responses remained statistically similar throughout the study period (P>0.05). CONCLUSION: The rate of telemedicine utilization in spine patients remains high, at approximately 1/3 of all visits, even after the initial peak coronavirus lockdown period. In addition, patient satisfaction with telemedicine remained consistent throughout the study period, regardless of pandemic restrictions on in-person visits. LEVEL OF EVIDENCE: III.

A study on the implementation of remote learning: Malaysian students' struggles and suggestions for improvement

This study seeks to explore Malaysian undergraduates' perspectives on the implementation of remote learning in their university during the period of the movement control order (MCO). Since teaching and learning activities have been impacted by the pandemic, it is imperative to consider students' perspectives on carrying out classes via the online platform as many studies claim that the pandemic has disrupted teaching and learning activities. A total of 1,028 undergraduate students participated in this voluntary study by answering an open-ended survey sent out to their student email addresses during the MCO period that restricted students and lecturers from going to the university. The qualitative responses from the students were critically analyzed for thematic patterns. The four themes emerging from the data provide future teaching and learning plans that should embed self-learning techniques that could aid students if a similar predicament should hit us in the future. Course instructors can use this information to design future lessons that could assist their learners better. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

LFR Waiter: Arduino Based Android Application Controlled Waiter Robot

One of the most pressing challenges facing restaurants since the COVID-19 outbreak began is personnel. A staffing scarcity across the business has resulted in a slew of issues, including significantly longer wait times and irritated clients. A robot waiter may make a huge impact in a restaurant in this situation. This research led to the formation of a low-cost Arduino-based Android application control Robot that can work as a restaurant waiter. The proposed model can follow a path, avoid obstacles, serve meals to a specific consumer, and return to the kitchen on its own. To precisely follow the line, the PID algorithm is utilized. To detect potential obstructions, a sonar sensor is used. On an LCD, messages and warnings are displayed. An Android app that allows the chief to select a particular table for serving meals. For convenience, the robot's current state is displayed in the application. Our testing results show that the robot performs satisfactorily over 90% of the time. It should be emphasized that the offered model is adaptable to any restaurant. © 2022 IEEE.

Covid-19 Pandemic: Project-Based Learning as Interprofessional Learning Model to Improve Student With Special Needs’ Self Efficacy

There is a particular emphasis on education for special needs children. They have a right to an education that is appropriate to their needs. This study aimed to improve the self-efficacy of students with special needs who studied in inclusive elementary schools by using project-based learning. The research method used explorative qualitative with the interview, observation, and documentation instruments through triangulation. All instruments were analyzed in-depth, descriptive narrative. This research was carried out in the inclusive elementary schools, among others were five Public Elementary Schools (PES). The findings show that the learning approach of project-based learning at inclusive elementary schools in the covid-19 pandemic effectively improved the students with special need to be active, participatory and feel motivated to solve learning problems experienced by learning products produced. © 2022 Universidad de Valladolid. All rights reserved.

COVID-19 and fiscal stimulus in South Asia: implications for resilience and sustainable development

The world continues to be gripped by COVID-19, though the pandemic's impact varies across countries and regions. The South Asian case is illustrative. The region is marked by inherent socioeconomic and other vulnerabilities, including high population density, relatively poor health care, and limited water sanitation facilities. South Asian countries also evince varied levels of damage from the pandemic. This chapter examines the region's circumstances as of November 2020, using macroeconomic data to explore varied pandemic impacts and fiscal policy responses. We also discuss the COVID-19 fund formed at the South Asian regional level with contributions from all eight South Asian countries. Our analysis includes each country's external and internal share of fiscal stimulus, and the implications for sustainable development goals. In an argument for integrating resilience and development frameworks, the chapter analyzes Japan's example of national resilience planning and related sustainable development frameworks. Our research indicates that a sustainable recovery is advantaged by fiscal stimulus that can be linked to extant developmental frameworks. © 2022 Elsevier Inc. All rights reserved.

ML and DL Architectures Comparisons for the Classification of COVID-19 Using Chest X-Ray Images

Pandemic COVID-19 ranked as one of the world’s worst pandemics ever witnessed in history. It has affected every country by spreading this disease with an increase in mortality at alarming rates despite the technologically advanced era of medicine. AI/ML is one of the strong wings in the medical field so while fighting the battle to control and diagnose the best medicine for the outbreak COVID-19 disease. Automated and AI-based prediction models for COVID-19 are the main attraction for the scientist hoping to support some good medical decisions at this difficult time. However, mostly classical image processing methods have been implemented to detect COVID-19 cases resultant in low accuracy. In this chapter, multiple naïve machine and deep learning architectures are implied to evaluate the performance of the models for the classification of COVID-19 using a dataset comprising of chest x-ray images of, i.e., COVID-19 patients and normal (non-infected) individuals. The analysis looks at three machine learning architectures including Logistic Regression, Decision Tree (DT) Classifier, and support vector machine (SVM), and four deep learning architectures, namely: Convolutional neural networks (CNNs), VGG19, ResNet50, and AlexNet. The dataset has been divided into train, test and validation set and the same data have been used for the training, testing, and validation of all the architectures. The result analysis shows that AlexNet provides the best performance out of all the architectures. It can be seen that the AlexNet model achieved 98.05% accuracy (ACC), 97.40% recall, 98.03% F1-score, 98.68% precision, and 98.05% area under the curve (AUC) score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Mathematical modeling on the transmission of COVID-19 and its reproduction numbers in SAARC countries

In the middle of December 2019, a virus known as coronavirus (COVID-19) generated by severe acute respiratory syndrome corona virus 2 (SARC-CoV-2) was first detected in Wuhan, Hubei Province, China. As of the 9th of March, 2022, spread to over 212 countries, causing 429 million confirmed cases and 6 million people to lose their lives worldwide. In developing countries like the South Asian area, alarming dynamic variations in the pattern of confirmed cases and death tolls were displayed. During epidemics, accurate assessment of the characteristics that characterize infectious disease transmission is critical for optimizing control actions, planning, and adapting public health interventions. The reproductive number, or the typical number of secondary cases caused by an infected individual, can be employed to determine transmissibility. Several statistical and mathematical techniques have been presented to calculate across the duration of an epidemic. A technique is provided for calculating epidemic reproduction numbers. It is a MATLAB version of the EpiEstim package's R function estimate R, version 2.2-3. in the South Asian Association for Regional Cooperation (SAARC) countries. The three methodologies supported are 'parametric SI,' 'non-parametric SI,' and 'uncertain SI.' The present study indicated that the highest reproduction number was 12.123 and 11.861 on 5th and 14th March 2020 in India and Sri_Lanka, whereas the lowest reproduction number was the lowest was 0.300 and 0.315 in Sri_Lanka and India. The Maximum and minimum reproductive number of Bangladesh was 3.752 and 0.725. In this study, we have tried to point out the worst, best and current situation of SAARC countries.

Estimation of the sars-cov-2 specific reproduction number in saarc countries: A 60-days data-driven analysis

Novel coronavirus disease-2019 (COVID-19) was acknowledged as a global pandemic by WHO, which was first observed at the end of December 2019 in Wuhan city, China, caused by extreme acute respiratory syndrome coronavirus2 (SARS-CoV-2). According to the Weekly operation Update on COVID-19 (November 13, 2020) of the World Health Organization, more than 53 million confirmed cases are reported, including 1.3 million deaths. Various precautionary measures have been taken worldwide to reduce its transmission, and extensive researches are going on. The purpose of this analysis was to determine the initial number of reproductions (Ro) of the coronavirus of SAARC countries named Afghanistan, Bangladesh, India, Pakistan, Bhutan, Nepal, the Maldives, and Sri-Lanka for the first 60 days as the growth is exponential in the early 60 days. The reproduction numbers of coronavirus for Afghanistan, Bangladesh, India, Pakistan, Bhutan, the Maldives, Nepal, and Sri Lanka are 1.47, 3.86, 2.07, 1.43, 1.31, 3.22, 1.75, and 2.39 respectively. The basic reproduction number (R0) 3.86 for Bangladesh and 1.31 for Bhutan indicated that up to 60-days of the outbreak COVID-19, the epidemic was more severe in Bangladesh and less severe in Bhutan among all the SAARC countries. Our predictions can be helpful in planning alertness and taking the appropriate measures to monitor it. ©: Author (s).

Pre- and in-hospital anticoagulation therapy in coronavirus disease 2019 patients: a propensity-matched analysis of in-hospital outcomes.

AIMS: To estimate if chronic anticoagulant (CAC) treatment is associated with morbidity and mortality outcomes of patients hospitalized for SARS-CoV-2 infection. METHODS: In this European multicentric cohort study, we included 1186 patients of whom 144 were on CAC (12.1%) with positive coronavirus disease 2019 testing between 1 February and 30 July 2020. The average treatment effect (ATE) analysis with a propensity score-matching (PSM) algorithm was used to estimate the impact of CAC on the primary outcomes defined as in-hospital death, major and minor bleeding events, cardiovascular complications (CCI), and acute kidney injury (AKI). We also investigated if different dosages of in-hospital heparin were associated with in-hospital survival. RESULTS: In unadjusted populations, primary outcomes were significantly higher among CAC patients compared with non-CAC patients: all-cause death (35% vs. 18% P < 0.001), major and minor bleeding (14% vs. 8% P = 0.026; 25% vs. 17% P = 0.014), CCI (27% vs. 14% P < 0.001), and AKI (42% vs. 19% P < 0.001). In ATE analysis with PSM, there was no significant association between CAC and primary outcomes except for an increased incidence of AKI (ATE +10.2%, 95% confidence interval 0.3-20.1%, P = 0.044). Conversely, in-hospital heparin, regardless of dose, was associated with a significantly higher survival compared with no anticoagulation. CONCLUSIONS: The use of CAC was not associated with the primary outcomes except for the increase in AKI. However, in the adjusted survival analysis, any dose of in-hospital anticoagulation was associated with significantly higher survival compared with no anticoagulation.

Novel COVID-19 Recognition Framework Based on Conic Functions Classifier

The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.

SARS-CoV-2 Viral Load Is Correlated With the Disease Severity and Mortality in Patients With Cancer.

The correlation between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) viral load and risk of disease severity in cancer patients is poorly understood. Given the fact that cancer patients are at increased risk of severe coronavirus disease 2019 (COVID-19), analysis of viral load and disease outcome in COVID-19-infected cancer patients is needed. Here, we measured the SARS-CoV-2 viral load using qPCR cycle threshold (Ct) values collected from 120 noncancer and 64 cancer patients' nasopharyngeal swab samples who are admitted to hospitals. Our results showed that the in-hospital mortality for high viral load cancer patients was 41.38%, 23.81% for medium viral load and 14.29% for low viral load patients (p < -0.01). On the other hand, the mortality rate for noncancer patients was lower: 22.22% among patients with high viral load, 5.13% among patients with medium viral load, and 1.85% among patients with low viral load (p < 0.05). In addition, patients with lung and hematologic cancer showed higher possibilities of severe events in proportion to high viral load. Higher attributable mortality and severity were directly proportional to high viral load particularly in patients who are receiving anticancer treatment. Importantly, we found that the incubation period and serial interval time is shorter in cancer patients compared with noncancer cases. Our report suggests that high SARS-CoV-2 viral loads may play a significant role in the overall mortality and severity of COVID-19-positive cancer patients, and this warrants further study to explore the disease pathogenesis and their use as prognostic tools.

Door-to-balloon Time for ST-elevation MI in the Coronavirus Disease 2019 Era

In patients presenting with ST-elevation MI, prompt primary coronary intervention is the preferred treatment modality. Several studies have described improved outcomes in patients with door-to-balloon (D2B) and symptom onset-to-balloon (OTB) times of less than 2 hours, but the specific implications of the coronavirus disease 2019 (COVID-19) pandemic on D2B and OTB times are not well-known. This review aims to evaluate the impact of COVID-19 on D2B time and elucidate both the factors that delay D2B time and strategies to improve D2B time in the contemporary era. The search was directed to identify articles discussing the significance of D2B times before and during COVID-19, from the initialization of the database to December 1, 2020. The majority of studies found that onset-of-symptom to hospital arrival time increased in the COVID-19 era, whereas D2B time and mortality were unchanged in some studies and increased in others.

Outbreak of respiratory infection: Ncov-2019 current status and its impact on global health

Novel coronavirus-2019 (nCoV-2019) emerged as a potentially infectious respiratory disease caused by newly discovered β-coronavirus. nCoV-19 has emerged as a global pandemic due to the rapid transmission and high infection rate commonly involved in acute respiratory ill-ness. Literature search includes various databases like Google Scholar, PubMed, ScienceDirect, and Scopus for studies published using a different combination of keywords “coronavius”, “COVID-19”, “SARS”, “MERS”, “antiviral drugs”, “vaccines”, and “immunity”. We collected epidemiology data from the Worldometer portal (data available till 9 October, 2020). Fever, dry cough, dyspnea, sore throat, or fatigue are common clinical symptoms of the infection. Cytotoxic T-cells and T-helper cells plus Cytotoxic T cells (CD8+) account for maximum (approximately 80%) of total infiltrate in the pulmonary region of the affected nCoV individuals and act as a significant contributor to the clearance of the infection. This review intends to outline the literature con-cerning the mode of actual transmission, immune response, and possible therapeutic approach against the virus. © 2020 Bentham Science Publishers.

Impact of COVID 19 on FM services and strategies to overcome this situation in GCC countries

The Gulf Cooperation Council (GCC) countries) face the dual shock of a pandemic caused by the novel coronavirus (COIVD 19) and a collapse in oil prices. GCC countries many times experienced fluctuations in oil price and learnt how to deal the situation. However, the COVID-19 outbreak, being a new one, has created a lot of concern among GCC countries. This pandemic is causing turbulence to the economies of the GCC countries. Major industries that have been impacted in GCC countries due to COVID-19 pandemic include Energy, Aviation, Food & Beverage, Chemical, Retail & E-commerce, Travel & Tourism among others. Besides a major downfall in oil demand has been reported across the globe due to the effect of COVID-19. Due to this, many oil productions sites have been shut down or has to decrease production in the region. The closure of the industrial and commercial activities because of the pandemic would certainly affect their economies. Facility management (FM) constitutes a branch, jointly representing real estate market with property management and asset management. It plays a crucial role in economic activities in region as FM services are involved in all industrial and commercial activities. The Facility Management (FM) market in GCC countries has witnessed robust growth during the last few decades due to rapid economic activities in this region. It is an established fact in FM services manpower cost dominates the total cost whereas material cost plays vital role in construction industries. Majority of work forces in GCC countries in FM sector is migrant people from the Globe. CCC countries are showing actions that they are capable of acting effectively to contain the health and economic impacts of the pandemic within their own borders, albeit with marked shortcomings when it comes to protecting migrant workers. It is estimated that approximately 23 million migrant workers are living GCC countries . These millions of migrant workers across the Gulf face uncertainty as host countries lock down, employers withhold wages or mull redundancies, and strict coronavirus containment measures lead to deportations and confinement. This will have series impact on FM sector. In this paper a detail study the impact of COVID 19 on FM sector in GCC countries is reported. Strategies to overcome the crisis are listed along with the means and recommendation to implement the strategy.