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The COVID-19 pandemic necessitated sensitive, fast, and inexpensive testing for the virus in 2020 prior to the widespread availability of vaccines. Early testing efforts were limited by bottlenecks on reagents, low-throughput testing options, and the slow return of test results. In this paper, we detail the testing pipeline we established at the University of Wisconsin-Madison for rapid, inexpensive, and sensitive surveillance testing for SARS-CoV-2, and we highlight the strengths of the platform that would allow it to be applied to other disease surveillance projects, SARS-CoV-2 variant testing, or future pandemics. This pipeline can be quickly established for further accreditation and clinical application. [ABSTRACT FROM AUTHOR]

The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of this virus. Analysis and classification of newly determined S protein structures for SARS-CoV-2 are critical to properly understand their functional, evolutionary and architectural relatedness to already known protein structures. In this paper, first, the comparative analysis of SARS-CoV-2 S protein structures is performed. Through comparative analysis, the S protein structures in the PDB (protein data bank) database are compared and analyzed not only with each other but with the structures of other viruses for various parameters. Second, the S protein structures in PDB are classified into different variants, and the associated published literature is studied to investigate what kind of therapeutics (antibodies, T-cell receptors and small molecules) are used on the structures. This is the first study that classifies the S protein structures of the SARS-CoV-2 in PDB into various variants, and the obtained comparative analysis results could be beneficial to the research community, in general, and to crystallographers and health workers, in particular. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has been causing negative impacts on various sectors in Sri Lanka, as a result of the public health interventions that the government had to implement in order to reduce the spread of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This paper presents a mathematical epidemiological model, called SEQIJRDS, having additional compartments for quarantine and infected people divided into two compartments as diagnosed and non diagnosed, compared to the SEIR model. We have presented the rate equations for the model and the basic reproduction number is derived. This model considers the effect of vaccination, the viral load of the variants, mask use, mobility, contact tracing and quarantine, natural immunity development of the infected people, and immunity waning of the recovered group as key developments of the model. The model has been validated for the COVID-19 pandemic in Sri Lanka by parameter derivation using mathematical formulations with the help of the existing data, the literature, and by model fitting for historical data. We present a comparison of the model projections for hospitalized infected people, the cumulative death count, and the daily death count against the ground truth values and projections of the SEIR and SIR models during the model validation. The validation results show that the proposed SEQIJRDS model's 12-week projection performance is significantly better than both the SEIR and SIR models; the 2-, 6-, 8-, and 10-week projection performance is always better, and the 4-week projection performance is only slightly inferior to other models. Using the proposed SEQIJRDS model, we project mortality under different lockdown procedures, vaccination procedures, quarantine practices, and different mask-use cases. We further project hospital resource usage to understand the best intervention that does not exhaust hospital resources. At the end, based on an understanding of the effect of individual interventions, this work recommends combined public health interventions based on the projections of the proposed model. Specifically, three recommendations—called minimum, sub-optimum, and optimum recommendations—are provided for public health interventions. [ABSTRACT FROM AUTHOR]

A new class of coronavirus, known as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been discovered, which is responsible for the occurrence of the disease, COVID-19. A comparative study with SARS, MERS and other human viruses was conductedand concluded that SARS-CoV-2 spread more rapidly due to increased globalization and adaptation of the virus in every environment. According to recent WHO reports, by 16 May 2021, the current outbreak of COVID-19 had affected over 174,054,314 people and killed more than 3,744,116 people in more than 222 countries acrossthe world. Finding a solution against the deadly COVID-19 has become an enormous challenge for researchers and virologists. A ring vaccination trial, which recruits subjects connected to a known case either socially or geographically, is a solution to evaluate vaccine efficacy and control the spread of the disease simultaneously, although its implementation is challenging. This review aims to summarize the noteworthy features of the world-intimidating SARS-CoV-2 global pandemic along with its evaluation, problems and challenges in the treatment strategies, clinical efficiency and detection methods proposed so far. This paper describes the impact of the lockdown in response to the COVID-19 pandemic on social, economic, health, and National Health Programs in India; possible ways to control the disease are also discussed. [ABSTRACT FROM AUTHOR]

At the end of 2019, an outbreak of the novel coronavirus (COVID-19) made a profound impact on the country's production and people's daily lives. Up until now, COVID-19 has not been fully controlled all over the world. Based on the clinical research progress of infectious diseases, combined with epidemiological theories and possible disease control measures, this paper establishes a Susceptible Infected Recovered (SIR) model that meets the characteristics of the transmission of the new coronavirus, using the least square estimation (LSE) method to estimate the model parameters. The simulation results show that quarantine and containment measures as well as vaccine and drug development measures can control the spread of the epidemic effectively. As can be seen from the prediction results of the model, the simulation results of the epidemic development of the whole country and Nanjing are in agreement with the real situation of the epidemic, and the number of confirmed cases is close to the real value. At the same time, the model's prediction of the prevention effect and control measures have shed new light on epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a globally leading public health concern over the past two years. Despite the development and administration of multiple vaccines, the mutation of newer strains and challenges to universal immunity has shifted the focus to the lack of efficacious drugs for therapeutic intervention for the disease. As with SARS-CoV, MERS-CoV, and other non-respiratory viruses, flavonoids present themselves as a promising therapeutic intervention given their success in silico, in vitro, in vivo, and more recently, in clinical studies. This review focuses on data from in vitro studies analyzing the effects of flavonoids on various key SARS-CoV-2 targets and presents an analysis of the structure-activity relationships for the same. From 27 primary papers, over 69 flavonoids were investigated for their activities against various SARS-CoV-2 targets, ranging from the promising 3C-like protease (3CLpro) to the less explored nucleocapsid (N) protein; the most promising were quercetin and myricetin derivatives, baicalein, baicalin, EGCG, and tannic acid. We further review promising in silico studies featuring activities of flavonoids against SARS-CoV-2 and list ongoing clinical studies involving the therapeutic potential of flavonoid-rich extracts in combination with synthetic drugs or other polyphenols and suggest prospects for the future of flavonoids against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

COVID-19 is an emerging disease of global public health concern. As the pandemic overwhelmed emergency departments (EDs), a restructuring of emergency care delivery became necessary in many hospitals. Furthermore, with more than 2000 papers being published each week, keeping up with ever-changing information has proven to be difficult for emergency physicians. The aim of the present review is to provide emergency physician with a summary of the current literature regarding the management of COVID-19 patients in the emergency department. [ABSTRACT FROM AUTHOR]

"Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", the novel coronavirus, is responsible for the ongoing worldwide pandemic. "World Health Organization (WHO)" assigned an "International Classification of Diseases (ICD)" code—"COVID-19"-as the name of the new disease. Coronaviruses are generally transferred by people and many diverse species of animals, including birds and mammals such as cattle, camels, cats, and bats. Infrequently, the coronavirus can be transferred from animals to humans, and then propagate among people, such as with "Middle East Respiratory Syndrome (MERS-CoV)", "Severe Acute Respiratory Syndrome (SARS-CoV)", and now with this new virus, namely "SARS-CoV-2", or human coronavirus. Its rapid spreading has sent billions of people into lockdown as health services struggle to cope up. The COVID-19 outbreak comes along with an exponential growth of new infections, as well as a growing death count. A major goal to limit the further exponential spreading is to slow down the transmission rate, which is denoted by a "spread factor (f)", and we proposed an algorithm in this study for analyzing the same. This paper addresses the potential of data science to assess the risk factors correlated with COVID-19, after analyzing existing datasets available in "ourworldindata.org (Oxford University database)", and newly simulated datasets, following the analysis of different univariate "Long Short Term Memory (LSTM)" models for forecasting new cases and resulting deaths. The result shows that vanilla, stacked, and bidirectional LSTM models outperformed multilayer LSTM models. Besides, we discuss the findings related to the statistical analysis on simulated datasets. For correlation analysis, we included features, such as external temperature, rainfall, sunshine, population, infected cases, death, country, population, area, and population density of the past three months—January, February, and March in 2020. For univariate timeseries forecasting using LSTM, we used datasets from 1 January 2020, to 22 April 2020. [ABSTRACT FROM AUTHOR]