Background The COVID-19 pandemic has already claimed considerable lives. There are major concerns in Africa due to existing high prevalence rates for both infectious and non-infectious diseases and limited resources in terms of personnel, beds and equipment. Alongside this, concerns that lockdown and other measures will have on prevention and management of other infectious diseases and non-communicable diseases (NCDs). NCDs are an increasing issue with rising morbidity and mortality rates. The World Health Organization (WHO) warns that a lack of nets and treatment could result in up to 18 million additional cases of malaria and up to 30,000 additional deaths in sub-Saharan Africa. Objective Document current prevalence and mortality rates from COVID-19 alongside economic and other measures to reduce its spread and impact across Africa. In addition, suggested ways forward among all key stakeholder groups. Our approach Contextualise the findings from a wide range of publications including internet-based publications coupled with input from senior-level personnel. Ongoing activities Prevalence and mortality rates are currently lower in Africa than among several Western countries and the USA. This could be due to a number of factors including early instigation of lockdown and border closures, the younger age of the population, lack of robust reporting systems and as yet unidentified genetic and other factors. Innovation is accelerating to address concerns with available equipment. There are ongoing steps to address the level of misinformation and its consequences including fines. There are also ongoing initiatives across Africa to start addressing the unintended consequences of COVID-19 activities including lockdown measures and their impact on NCDs including the likely rise in mental health disorders, exacerbated by increasing stigma associated with COVID-19. Strategies include extending prescription lengths, telemedicine and encouraging vaccination. However, these need to be accelerated to prevent increased morbidity and mortality. Conclusion There are multiple activities across Africa to reduce the spread of COVID-19 and address misinformation, which can have catastrophic consequences, assisted by the WHO and others, which appear to be working in a number of countries. Research is ongoing to clarify the unintended consequences given ongoing concerns to guide future activities. Countries are learning from each other.

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Response to the Novel Corona Virus (COVID-19) Pandemic Across Africa: Successes, Challenges, and Implications for the Future

Coronavirus disease 2019 (COVID-19), a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 235 million individuals and led to more than 4.8 million deaths worldwide as of October 5 2021. Cryo-electron microscopy and topology show that the SARS-CoV-2 genome encodes lots of highly glycosylated proteins, such as spike (S), envelope (E), membrane (M), and ORF3a proteins, which are responsible for host recognition, penetration, binding, recycling and pathogenesis. Here we reviewed the detections, substrates, biological functions of the glycosylation in SARS-CoV-2 proteins as well as the human receptor ACE2, and also summarized the approved and undergoing SARS-CoV-2 therapeutics associated with glycosylation. This review may not only broad the understanding of viral glycobiology, but also provide key clues for the development of new preventive and therapeutic methodologies against SARS-CoV-2 and its variants.

The glycosylation in SARS-CoV-2 and its receptor ACE2.

The Corona Pandemic has forced many organizations to limit human interactions by implementing what so-called Work-from-Home (WFH). This study aims to investigate the role of Authentic Leadership, P...

https://www.tandfonline.com/doi/pdf/10.1080/23311975.2021.1885573

Working from home during the corona pandemic: Investigating the role of authentic leadership, psychological capital, and gender on employee performance

A novel coronavirus-2 (SARS-CoV-2) was first identified in Wuhan, China, and quickly spread globally. Several treatments have been proposed, many of which have proven ineffective. Consequently, there is a need to review the published evidence of drug clinical trials to guide future prescribing. A systematic review of published clinical trials and retrospective observational studies was carried out. The search was made using PubMed, Embase, MEDLINE, and China National Knowledge Infrastructure (CNKI) databases. Articles published between January 2020 and October 2020 and written in the English language were retrieved and included in the study. Researches that used traditional medicine, in-vitro and in-vivo animal studies, as well as reviews were excluded. Seventy-three relevant articles that fulfilled the inclusion criteria were finally selected and reviewed. Hydroxychloroquine, chloroquine, and azithromycin produced no clinical evidence of efficacy in randomized controlled clinical trials (RCT). However, retrospective observational studies reported the efficacy of remdesivir and lopinavir/ritonavir in reducing viral load, although there have been concerns with lopinavir/ritonavir and, more recently, remdesivir. Recently, tocilizumab, dexamethasone, and methylprednisolone significantly relieved lung inflammation and decreased mortality in patients with severe COVID-19. In addition, convalescent plasma was effective in boosting strong immunity among patients with mild COVID-19. There is currently no single worldwide approved therapeutic option for patients with COVID-19 despite the initial hype with medicines, including hydroxychloroquine. Nonetheless, dexamethasone has shown promise in symptomatic treatment and convalescent plasma in boosting immunity. New treatments are currently being researched, and the findings will be reported accordingly to provide evidence-based guidance for prescribers and policymakers.

/pdf/systematic-review-on-the-therapeutic-options-for-covid-19-46jmmgkpuy.pdf

Systematic Review on the Therapeutic Options for COVID-19: Clinical Evidence of Drug Efficacy and Implications

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

/pdf/a-systematic-review-on-ai-ml-approaches-against-covid-19-25j88mny6u.pdf

A systematic review on AI/ML approaches against COVID-19 outbreak

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the seventh-generation coronavirus family causing viral pandemic coronavirus disease (COVID-19) across globe affecting millions of people. The objectives of this study are to (1) identify the major research themes in COVID-19 literature, (2) determine the origin, symptoms and modes of transmission of COVID, (3) recommend the intervention and mitigation strategies adopted by the Governments globally against the spread of COVID-19 and the traumatization among the public? and (4) study the possible drugs/treatment plans against COVID-19. A systematic literature review and comprehensive analysis of 38 research articles on COVID-19 are conducted. An integrated Research focus parallel-ship network and keyword co-occurrence analysis are carried out to visualize the three research concepts in COVID-19 literature. Some of our observations include: (1) as SARS-CoV-2’s RNA matches ~ 96% to SARS-CoV, it is assumed to be transmitted from the bats. (2) The common symptoms are high fever, dry cough, fatigue, sputum production, shortness of breath, diarrhoea etc. (3) A lockdown across 180 affected counties for more than a month with social-distancing and the precautions taken in SARS and MERS are recommended by the Governments. (4) Researchers’ claim that nutrition and immunity enhancers and treatment plans such as arbidol, lopinavir/ritonavir, convalescent plasma and mesenchymal stem cells and drugs including remdesivir, hydroxychloroquine, azithromycin and favipiravir are effective against COVID-19. This complied report serves as guide to help the administrators, researchers and the medical officers to adopt recommended intervention strategies and the optimal treatment/drug against COVID-19.

/pdf/systematic-literature-review-on-novel-corona-virus-sars-cov-gre67785ly.pdf

Systematic literature review on novel corona virus SARS-CoV-2: a threat to human era

COVID-19 pandemic disease spread by SARS-COV-2 single-strand structure RNA virus belongs to the 7th generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissibility has put many counties under lockdown. With a cure for the infection uncertain in the near future, the pressure currently lies in the current healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research seeks to understand the spreading patterns of the COVID-19 virus through exponential growth modelling and identifies countries that have showed an initial sign of containment until 26th March 2020. Post identification of countries that have shown an initial sign of containment, predictive supervised machine learning models were built with infrastructure, environment, policies, and infection related independent variables. For the purpose, COVID-19 infection data across 42 countries were used. Logistic regression results shows a positive significant relationship of healthcare infrastructure and lockdown policies on the sign of early containment in countries. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines were developed and are seen to have accuracies between 76.2% to 92.9% to predict early sign of infection containment. Other policies and activities taken by countries to contain the infection are also discussed.

Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Initial Containment using Machine learning

S. P. Sathiya Prabhakaran

Papers

Systematic literature review on novel corona virus SARS-CoV-2: a threat to human era

Exploring the Growth of COVID-19 Cases using Exponential Modelling Across 42 Countries and Predicting Signs of Initial Containment using Machine learning