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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]

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]

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]

Advantageous variants of the SARS-CoV-2 virus have arisen through mutations, particularly on a single amino acid basis. These point mutations can cause changes in the structure of SARS-CoV-2 and affect the efficiency of interaction with the ACE2 protein. N501Y and E484K mutations affecting binding by ACE2 have been widely observed. This study aimed to predict SARS-CoV-2 mutations that could be as effective as N501Y and E484K and pose a danger due to their high contagiousness. Experimental data on SARS-CoV-2 and ACE2 binding and stability were associated with different amino acid properties and integrated into machine learning and computational biology techniques. As a result of the analyses made in algorithms, N501M, Q414A, N354K, Q498H and N460K have been predicted to be likely to have a dangerous effect. The N501W mutations are most likely to have dangerous effects on the spread of the coronavirus. We suggest that attention should be paid to the position 501 mutation since this position is repeated in the lists of mutations that the algorithm detected as dangerous. G446, G447, Y505, T500, Q493, Y473, and G476 were determined as the positions where dangerous variants could be seen as a result of the analyses of the multiple interaction data created with the ACE2 and RBD interaction data. The 13 dangerous positions and mutations have been detected to accurately describe the position of the mutations caused by the Omicron variant and were among the known dangerous mutations similar to those occurring at Q498, G446, Y505 and Q493 positions. [ABSTRACT FROM AUTHOR]

One of the most pressing challenges associated with SARS treatment is the emergence of new variants that may be transmissible, causing more severe disease or being resistant to the current standard of treatment. This study aimed to identify potential drug candidates from novel imidazole derivatives against SARS-CoV-2 main protease (Mpro), a crucial drug target for treating viral infection, using a computational approach that integrated molecular docking and dynamics simulation. In this study, we utilized AutoDock Vina within the PyRx workspace for molecular docking analysis to explore the inhibitory effects of the compounds on the Mpro, a drug target for SARS-CoV-2. The ADMET properties of these compounds, including absorption, distribution, metabolism, excretion, and toxicity, were evaluated using the SwissADME and ADMETLab servers. Each of the 18 compounds that were tested demonstrated strong binding affinities towards Mpro, with imidazolyl–methanone C10 showing the most significant binding affinity. Moreover, pyridyl–imidazole C5, thiophenyl–imidazole C1, and quinoline–imidazole C14 displayed binding affinities of −8.3, −8.2, and −7.7 Kcal/mol, respectively. These compounds interacted with specific amino acid residues (HIS A:41—CYS A:145) within the Mpro protein. To assess the stability of the ligand with the best binding affinity, molecular dynamics (MD) simulations were conducted using Schrodinger software, which revealed its stability over the simulation period. The study provides valuable insights into the potential of imidazole derivatives as SARS-CoV-2 Mpro inhibitors. All compounds including C10 display promising characteristics and hold potential as drug candidates for SARS-CoV-2. However, further optimization and experimental validation of these compounds are necessary to advance their development as effective therapeutics against viral infections. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on countries worldwide, including the United Kingdom (UK). The UK has faced numerous challenges, but its response, including the rapid vaccination campaign, has been noteworthy. While progress has been made, the study of the pandemic is important to enable us to properly prepare for future epidemics. Collaboration, vigilance, and continued adherence to public health measures will be crucial in navigating the path to recovery and building resilience for the future. In this article, we propose an overview of the COVID-19 situation in the UK using both mathematical (a nonlinear differential equation model) and statistical (time series modeling on a moving window) models on the transmission dynamics of the COVID-19 virus from the beginning of the pandemic up until July 2022. This is achieved by integrating a hybrid model and daily empirical case and death data from the UK. We partition this dataset into before and after vaccination started in the UK to understand the influence of vaccination on disease dynamics. We used the mathematical model to present some mathematical analyses and the calculation of the basic reproduction number ( R 0 ). Following the sensitivity analysis index, we deduce that an increase in the rate of vaccination will decrease R 0 . Also, the model was fitted to the data from the UK to validate the mathematical model with real data, and we used the data to calculate time-varying R 0 . The homotopy perturbation method (HPM) was used for the numerical simulation to demonstrate the dynamics of the disease with varying parameters and the importance of vaccination. Furthermore, we used statistical modeling to validate our model by performing principal component analysis (PCA) to predict the evolution of the spread of the COVID-19 outbreak in the UK on some statistical predictor indicators from time series modeling on a 14-day moving window for detecting which of these indicators capture the dynamics of the disease spread across the epidemic curve. The results of the PCA, the index of dispersion, the fitted mathematical model, and the mathematical model simulation are all in agreement with the dynamics of the disease in the UK before and after vaccination started. Conclusively, our approach has been able to capture the dynamics of the pandemic at different phases of the disease outbreak, and the result presented will be useful to understand the evolution of the disease in the UK and future and emerging epidemics. [ABSTRACT FROM AUTHOR]

Housing conditions can increase health risks for people who use opioids (PWUO). Little research documents the influence of housing on PWUO's ability to practice disease prevention methods. This study examines associations between housing status of PWUO in NYC and their ability to practice COVID-19 prevention recommendations during the initial wave of the pandemic. Participants were recruited via convenience sampling and administered a survey. Eligibility required age ≥18 years, using opioids ≥3times in the prior 30 days, and accessing a health-related service in the prior year. Descriptive and bivariate statistics assessed relationships between housing and the ability to practice social distancing, access soap and running water or hand sanitizer (soap), and access face masks. Multivariable Poisson regression was used to examine relationships between housing and the potential to practice COVID-19 prevention recommendations. The 329 participants were grouped into stable housing (34.3%), unstable/shelter housing (31.9%), and street homeless (33.7%) categories. Street homeless PWUO were significantly less likely to have access to soap and face masks than those stably housed. There were no significant differences between PWUO experiencing unstable and stable housing. PWUO experiencing street homelessness may have had increased vulnerability to COVID-19. Providing low-threshold health-related resources and increasing linkages to housing opportunities could promote health of PWUO experiencing street homelessness during future emergencies. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) is an infectious respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease, first identified in the Chinese city of Wuhan in November 2019, has since spread worldwide, is the latest human pandemic and has officially infected over 800 million people and has caused nearly seven million deaths to date. Although SARS-CoV-2 belongs to the large family of coronaviruses, it has some unique biological characteristics in its interplay with the human host. Therefore, this narrative review aims to provide an up-to-date overview of the structure of the virus, incubation and shedding in the human host, infectivity and biological evolution over time, as well as the main mechanisms for invading human host cells and replicating within. We also proffer that ongoing epidemiological surveillance of newly emerged variants must always be accompanied by biological studies aimed at deciphering new advantageous traits that may contribute to increasing virulence and pathogenicity, such that the most appropriate strategies for establishing a (relatively) safe coexistence with the human host can be implemented. [ABSTRACT FROM AUTHOR]

What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, unemployment, and Gross Domestic Product (GDP) in Africa by employing the Geographic Information System (GIS), multivariate analysis of covariance (MANCOVA), and spatial statistics. The entire dataset was analyzed using Stata, ArcGIS, and R software. The result shows (1) that there is evidence of a spatial pattern of COVID-19 cases and death rate clustering behavior in Africa, verifying the existence of spatial autocorrelation. The result also reveals (2) that COVID-19 has a negative effect on unemployment, inflation, and GDP in Africa. We confirmed that (3) temperature, rainfall, and humidity were statistically significantly associated with the spread of the COVID-19 pandemic in Africa. The comparison of the GDP of African countries before and after the pandemic shows (4) a large decrease in GDP, the highest in Seychelles (23 percent). The result of the study shows (5) that there has been a significant increase in inflation and unemployment rates in all countries since the outbreak of the pandemic as compared to the time before the outbreak. There is also evidence that (6) there is a significant relationship between death rate due to COVID-19 and population density; temperature with COVID-19 cases and death rate; and precipitation with death rate due to COVID-19. Therefore, respective governments and the international community need to pay attention to controlling/reducing the impact of COVID-19 on inflation, unemployment, and GDP, focusing on the indicated demographic and environmental variables. [ABSTRACT FROM AUTHOR]

A correlation between neutralization activity after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination and protection against coronavirus disease 2019 (COVID-19) has been demonstrated by several studies. Here, we detect SARS-CoV-2 neutralizing antibody (NAB) production in COVID-19 patients from the Steve Biko Academic Hospital complex (SBAH), South Africa (SA). Samples from COVID-19 patients (mild to severe) were collected. SARS-CoV-2 rapid assays, genotyping (Delta and Omicron variants) and enzyme-linked immunosorbent assays (ELISA) were performed. IBM® Statistical Package for the Social Sciences (SPSS®) version 28 was used for inferential statistical analysis, and the data were presented using the Prism9 software (version 9.4.1). A total of 137 laboratory-confirmed COVID-19 patients, 12 vaccine recipients and 8 unvaccinated participants were evaluated. The production of SARS-CoV-2 NABs was observed in some of the COVID-19 cases, mainly in severe cases, although this should be noted with caution due to the small sample size of this pilot study. NABs were also observed in asymptomatic participants, with the most being found in recipients (n = 6) of the BNT162b2 (Pfizer-BioNTech) COVID-19 vaccine. We found a strong presence of NABs in COVID-19 patients, specifically in mild and severe cases. Severe infection was associated with higher NAB production (82%). [ABSTRACT FROM AUTHOR]

It is important to know the community coping strategies during the rapid uprise of a pandemic, as this helps to predict the consequences, especially in the mental health spectrum. This study aims to explore coping strategies used by Bangladeshi citizens during the major wave of the COVID-19 pandemic. Design: Prospective, cross-sectional survey of adults living in Bangladesh. Methods: Participants were interviewed for socio-demographic data and completed the Bengali-translated Brief-COPE Inventory. COPING indicators were categorized in four ways, such as approach, avoidant, humor, and religion. Results: Participants (N = 2001), aged 18 to 86 years, were recruited from eight administrative divisions within Bangladesh (mean age 31.85 ± 14.2 years). The male-to-female participant ratio was 53.4% (n = 1074) to 46.6% (n = 927). Higher scores were reported for approach coping styles (29.83 ± 8.9), with lower scores reported for avoidant coping styles (20.83 ± 6.05). Humor coping scores were reported at 2.68 ± 1.3, and religion coping scores at 5.64 ± 1.8. Both men and women showed similar coping styles. Multivariate analysis found a significant relationship between male gender and both humor and avoidant coping (p < 0.01). Male gender was found to be inversely related to both religion and approach coping (p < 0.01). Marital status and education were significantly related to all coping style domains (p < 0.01). The occupation was related to approach coping (p < 0.01). Rural and urban locations differed in participants' coping styles (p < 0.01). Exploratory factor analysis revealed two cluster groups (factors 1 and 2) of mixed styles of coping. Conclusions: Participants in this study coped with the COVID-19 pandemic by utilizing mixed coping strategies. This study finds female gender, the married, elderly, and rural populations were adaptive to positive approaches to coping, whereas the male and educated population had the avoidant approach to coping. [ABSTRACT FROM AUTHOR]

When confronted with a public health emergency, significant innovative treatment protocols can sometimes be discovered by medical doctors at the front lines based on repurposed medications. We propose a statistical framework for analyzing the case series of patients treated with such new protocols, that enables a comparison with our prior knowledge of expected outcomes, in the absence of treatment. The goal of the proposed methodology is not to provide a precise measurement of treatment efficacy, but to establish the existence of treatment efficacy, in order to facilitate the binary decision of whether the treatment protocol should be adopted on an emergency basis. The methodology consists of a frequentist component that compares a treatment group against the probability of an adverse outcome in the absence of treatment, and calculates an efficacy threshold that has to be exceeded by this probability, in order to control the corresponding p-value and reject the null hypothesis. The efficacy threshold is further adjusted with a Bayesian technique, in order to also control the false positive rate. A random selection bias threshold is then calculated from the efficacy threshold to control for random selection bias. Exceeding the efficacy threshold establishes the existence of treatment efficacy by the preponderance of evidence, and exceeding the more demanding random selection bias threshold establishes the existence of treatment efficacy by the clear and convincing evidentiary standard. The combined techniques are applied to case series of high-risk COVID-19 outpatients that were treated using the early Zelenko protocol and the more enhanced McCullough protocol. [ABSTRACT FROM AUTHOR]

In the past, epidemics such as AIDS, measles, SARS, H1N1 influenza, and tuberculosis caused the death of millions of people around the world. In response, intensive research is evolving to design efficient drugs and vaccines. However, studies warn that new pandemics such as Coronavirus (COVID-19), variants, and even deadly pandemics can emerge in the future. The existing epidemic confinement approaches rely on a large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size-fits-all control technique, which might not be effective. To overcome this, in this work, we develop a game-theory-inspired approach that considers societal and economic impacts and formulates epidemic control as a non-zero-sum game. Further, the proposed approach considers the demographic information that provides a tailored solution to each demography. We explore different strategies, including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations, and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on the economy. To facilitate scalability, we propose a novel graph learning approach, which learns from the previously obtained COVID-19 game outputs and mobility rates of one state (region) depending on the other to produce an optimal solution. Our optimal solution is strategized to restrict the mobility between states based on the impact they are causing on COVID-19 spread. We aim to control the COVID-19 spread by more than 50% and model a dynamic solution that can be applied to different strains of COVID-19. Real-world demographic conditions specific to each state are created, and an optimal strategic solution is obtained to reduce the infection rate in each state by more than 50%. [ABSTRACT FROM AUTHOR]

The COVID-19 global pandemic necessitated adequate compliance to safe and effective vaccinations developed against the disease. Vaccination reduces high infection, morbidity, and mortality rates, develops herd immunity, and alleviates overburdened healthcare systems and massive economic costs. COVID-19 also raised awareness about the importance of robust health systems, notably public health competence and the number and training of community health workers. Using the World Health Organization, Global Development of Applied Community Studies project, and other available cross-sectional secondary data on 89 countries, we found that the strength of community health training and research (CHTR), the importance of religion, and freedom score (political rights, civil liberties) are associated with COVID-19 full-vaccination rate. Significant bivariate correlations included per-capita-GDP, number of nurses, per-capita health spending, aged population, noncommunicable disease rate, life satisfaction, government response stringency, nonviolent activism, education, and strength of community development, urban planning, and liberation theology fields. Our assessment of CHTR contribution to the COVID-19 response revealed a great resource for effectively targeting vaccine-hesitant individuals and increasing vaccination rates. The results suggest that to motivate vaccine adherence countries need adequate community health workforce training and research, a population not hesitant to adhere to public health measures based on religion, and societal-level freedoms. [ABSTRACT FROM AUTHOR]