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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects host cells following binding with the cell surface ACE2 receptors, thereby leading to coronavirus disease 2019 (COVID-19). SARS-CoV-2 causes viral pneumonia with additional extrapulmonary manifestations and major complications, including acute myocardial injury, arrhythmia, and shock mainly in elderly patients. Furthermore, patients with existing cardiovascular comorbidities, such as hypertension and coronary heart disease, have a worse clinical outcome following contraction of the viral illness. A striking feature of COVID-19 pandemics is the high incidence of fatalities in advanced aged patients: this might be due to the prevalence of frailty and cardiovascular disease increase with age due to endothelial dysfunction and loss of endogenous cardioprotective mechanisms. Although experimental evidence on this topic is still at its infancy, the aim of this position paper is to hypothesize and discuss more suggestive cellular and molecular mechanisms whereby SARS-CoV-2 may lead to detrimental consequences to the cardiovascular system. We will focus on aging, cytokine storm, NLRP3/inflammasome, hypoxemia, and air pollution, which is an emerging cardiovascular risk factor associated with rapid urbanization and globalization. We will finally discuss the impact of clinically available CV drugs on the clinical course of COVID-19 patients. Understanding the role played by SARS-CoV2 on the CV system is indeed mandatory to get further insights into COVID-19 pathogenesis and to design a therapeutic strategy of cardio-protection for frail patients. [ABSTRACT FROM AUTHOR]

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future. [ABSTRACT FROM AUTHOR]

Unexpected and sudden spread of the novel Coronavirus disease (COVID-19) has opened up many scopes for researchers in the fields of biotechnology, health care, educational sectors, agriculture, manufacturing, service sectors, marketing, finance, etc. Hence, the researchers are concerned to study, analyze and predict the impact of infection of COVID-19. The COVID-19 pandemic has affected many fields, particularly the stock markets in the financial sector. In this paper, we have proposed an econometric approach and stochastic approach to analyze the stochastic nature of stock price before and during a COVID-19-specific pandemic period. For our study, we considered the BSE SENSEX INDEX closing pricing data from the Bombay Stock Exchange for the period before and during COVID-19. We have applied the statistical tools, namely descriptive statistics for testing the normal distribution of data, unit root test for testing the stationarity, and GARCH and stochastic model for measuring the risk, also investigated drift and volatility (or diffusion) coefficients of the stock price SDE by using R Environment software and formulated the 95% confidence level bound with the help of 500 times simulations. Finally, the results have been obtained from these methods and simulations are discussed. [ABSTRACT FROM AUTHOR]

Background: The global community has battled the spread of SAR-CoV-2 for almost 2 years, and the projection is that the virus may be recurrent like the seasonal flu. The SARS-CoV-2 pandemic disrupted activities within the food supply chain that cost billions of dollars globally. This has heightened concerns about fomite spread of the virus through surfaces. There is an urgent need to understand the risk portends by this virus along the produce supply chain with conditions (low temperature and high relative humidity) conducive to extended survival of the virus. Main body: Pre-dating SARS-CoV-2 are other types of coronaviruses that had lower infection and mortality rates. There are some similarities between the former and the new coronavirus, especially with regards to transmission modes and their survivability on surfaces. There is evidence of other coronaviruses' survival on surfaces for weeks. Currently, there are limited evidence-based studies to enlighten us on how the virus is transmitted within the produce supply chain. A few studies claim that the virus could spread through the cold supply chains. However, these are not sufficient to make a conclusive inference about the deadly SARS-CoV-2. Conclusions: This paper provides a succinct review of the literature on current understanding of the transmission, survivability, and risk SARS-CoV-2 portend to humans within the produce supply chain and calls for more evidence-based research to allay or alert us of the potential risk of fomite transmission of SARS-CoV-2. The paper also highlights examples of conventional and novel non-thermal inactivation and sanitation methods applicable to this type of virus. [ABSTRACT FROM AUTHOR]

The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome—millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional approaches to understanding the diversity, dynamics, and evolution of viruses, is nonetheless a rich resource for machine learning (ML) approaches as alternatives for extracting such important information from these data. It is of hence utmost importance to design a framework for testing and benchmarking the robustness of these ML models. This paper makes the first effort (to our knowledge) to benchmark the robustness of ML models by simulating biological sequences with errors. In this paper, we introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show from experiments on a wide array of ML models that some simulation-based approaches with different perturbation budgets are more robust (and accurate) than others for specific embedding methods to certain noise simulations on the input sequences. Our benchmarking framework may assist researchers in properly assessing different ML models and help them understand the behavior of the SARS-CoV-2 virus or avoid possible future pandemics. [ABSTRACT FROM AUTHOR]

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates. [ABSTRACT FROM AUTHOR]

This 21st century is notable for experiencing so many disturbances at economic, social, cultural, and political levels in the entire world. The outbreak of novel corona virus 2019 (COVID-19) has been treated as a Public Health crisis of global Concern by the World Health Organization (WHO). Various outbreak models for COVID-19 are being utilized by researchers throughout the world to get well-versed decisions and impose significant control measures. Amid the standard methods for COVID-19 worldwide epidemic prediction, easy statistical, as well as epidemiological methods have got more consideration by researchers and authorities. One main difficulty in controlling the spreading of COVID-19 is the inadequacy and lack of medical tests for detecting as well as identifying a solution. To solve this problem, a few statistical-based advances are being enhanced and turn into a partial resolution up-to some level. To deal with the challenges of the medical field, a broad range of intelligent based methods, frameworks, and equipment have been recommended by Machine Learning (ML) and Deep Learning. As ML and DL have the ability of identifying and predicting patterns in complex large datasets, they are recognized as a suitable procedure for producing effective solutions for the diagnosis of COVID-19. In this paper, a perspective research has been conducted in the applicability of intelligent systems such as ML, DL and others in solving COVID-19 related outbreak issues. The main intention behind this study is (i) to understand the importance of intelligent approaches such as ML and DL for COVID-19 pandemic, (ii) discussing the efficiency and impact of these methods in the prognosis of COVID-19, (iii) the growth in the development of type of ML and advanced ML methods for COVID-19 prognosis,(iv) analyzing the impact of data types and the nature of data along with challenges in processing the data for COVID-19,(v) to focus on some future challenges in COVID-19 prognosis to inspire the researchers for innovating and enhancing their knowledge and research on other impacted sectors due to COVID-19. [ABSTRACT FROM AUTHOR]

As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset's geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets' design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus (COVID-19) forced the governing bodies across the world to ban all kinds of travel involving the movement of people. However, the policymakers have been working hard to mobilize the movement of essential goods and services considering its importance in containing the pandemic. It signifies how important the establishment and maintenance of logistics and supply chain management (LSCM) operations are, both during the containment and the successive periods. Motivated with the paramount importance of LSCM operations during the rapid spread of the novel coronavirus (COVID-19) across the globe, this paper critically reviews the existing literature closely related to it. The main aim is to identify and enhance the understanding of the logistical characteristics that play a vital role during pandemics. The selection of the literature was done using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology. The classification of the selected literature was done using a tripartite framework. Results show that researchers have focused mostly on "Post-event" (48.24%) management of logistical operations followed by the "Pre-event" (31.76%) and least in the "Integrated" (20%.) approaches. Furthermore, the analysis of the results provided useful insights that are discussed in detail. Also, twelve key areas have been identified that need due attention to improve the overall efficiency of the LSCM operations. We believe that the findings from this paper would be useful to the decision-makers and other stakeholders, as far as, maintaining efficient LSCM operations during as well after the pandemics are concerned. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 global pandemic has spread throughout the world, posing an extremely dangerous health risk for almost everyone. While dealing with such a large-scale viral disease, the healthcare infrastructure is under strain. Young adults who were thought to have been clinically affected fared better than their older counterparts. This pandemic has affected millions of children, especially those from low-economic backgrounds, who are otherwise highly susceptible and underprivileged. Children of frontline workers and single parents face particular challenges. Children from disadvantaged backgrounds are more vulnerable to infection and may experience long-lasting negative effects of the pandemic, such as child labor, child trafficking, child marriage, sexual exploitation, and even death. To lessen the psychological negative effects of COVID-19 on children and adolescents, parents, physicians, psychologists, social workers, and hospital administrators, government and non-governmental organizations have essential responsibilities to play. Priority one is to ensure that all children from all socioeconomic strata have access to the necessities of life, including social security, health care, and education. Moreover, some positive changes may result from the global crisis. This research paper discusses the potential consequences of this pandemic. Summary: Some of the hypotheses being investigated while looking at the low case fatality rate among pediatric age groups include the peak of immunity and differences in immune system response. The vulnerability of the comorbid pediatric age group, on the other hand, is comparable to that of their older counterparts. During the severe acute respiratory syndrome and Middle Eastern respiratory syndrome outbreaks, similar results were observed. The inoculation of a mother during pregnancy was found to be effective in protecting her progeny. Results: This is a review article, thus not applicable. Conclusions: Children are quite discouraged when school is abruptly stopped, when planned outings are canceled, when they are confined to the house, and when they are afraid of the unknown with regard to the continuing epidemic. Even though almost all studies suggest that COVID-19 has a relatively mild clinical manifestation in children, one must be cautious due to the novel coronavirus's rapid mutation rate. More research is needed to determine the relationship between COVID-19 and pediatric age groups. [ABSTRACT FROM AUTHOR]

Background: Out-of-hospital cardiac arrest (OHCA) remains one of the leading causes of death worldwide, and bystander CPR with public-access defibrillation improves OHCA survival outcomes. The COVID-19 pandemic has posed many challenges for emergency medical services (EMS), including the suggestion of compression-only resuscitation and recommendations for complete personal protective equipment, which have created operational difficulties and prolonged response time. However, the risk factors affecting OHCA outcomes during the pandemic are poorly defined. This study aimed to assess the characteristics and outcomes of OHCA patients before and during the COVID-19 pandemic in Thailand. Methods: This single-center, retrospective cohort study used data from electronic medical records and EMS paper records. All OHCA patients who visited Ramathibodi Hospital's emergency department before COVID-19 (March 2018 to December 2019) and during COVID-19 (March 2020-December 2021) were identified, and the number of emergency department returns of spontaneous circulation (ED-ROSC) and characteristics in OHCA patients before and during the COVID-19 pandemic in Thailand were collected. Results: A total of 136 patients were included (78 men [59.1%]; mean [SD] age, 67.9 [18] years); 60 of these were during the COVID-19 period (beginning March 2020), and 76 were before the COVID-19 period. The overall baseline characteristics that differed significantly between the two groups were bystander witness and mode of chest compression (p-values < 0.001 and < 0.001, respectively). The ED ROSC during the COVID-19 period was significantly lower than before the COVID-19 period (26.67% vs. 46.05%, adjusted OR 0.21, p-value < 0.001). There were significant differences in survival to admission between the COVID-19 period and before (25.00% and 40.79%, adjusted OR 0.26, p-value 0.005). However, 30-day survivals were not significantly different (3.3% during the COVID-19 period and 10.53% before the COVID-19 period). Conclusions: During the COVID-19 pandemic in Thailand, ED ROSC and survival to admission in out-of-hospital cardiac arrest patients were significantly reduced. Additionally, the witness responses and mode of chest compression were very different between the two groups. Trial registration: This trial was retrospectively registered on 7 December 2021 in the Thai Clinical Trial Registry, identification number TCTR20211207006. [ABSTRACT FROM AUTHOR]

This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title "Modeling Corona Spread Using Big Data Analytics." The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease's ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual's exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. [ABSTRACT FROM AUTHOR]

Since March of 2020, billions of people worldwide have been asked to limit their social contacts in an effort to contain the spread of the SARS-CoV-2 virus. However, little research has been carried out to date on the impact of such social distancing measures on the social isolation levels of the population. In this paper, we study the impact of the pandemic on the social isolation of the Spanish population, by means of 32,359 answers to a citizen survey collected over a period of 7 months. We uncover (1) a significant increase in the prevalence of social isolation in the population, reaching almost 26%; (2) gender and age differences, with the largest prevalence of isolation among middle-aged individuals; (3) a strong relationship between economic impact and social isolation; and (4) differences in social isolation, depending on the number of COVID-19 protection measures and on the perception of coronavirus infection risk by our participants. Our research sheds quantitative light on the sociological impact of the pandemic, and enables us to identify key factors in the interplay between the deployment of non-pharmaceutical interventions to contain the spread of an infectious disease and a population's levels of social isolation. [ABSTRACT FROM AUTHOR]

The outbreak of Coronavirus Disease 2019 (COVID-19) at the end of 2019 turned into a global pandemic. To help analyze the spread and evolution of the virus, we collated and analyzed data related to the viral genome, sequence variations, and locations in temporal and spatial distribution from GISAID. Information from the Wikipedia web page and published research papers were categorized and mined to extract epidemiological data, which was then integrated with the public dataset. Genomic and epidemiological data were matched with public information, and the data quality was verified by manual curation. Finally, an online database centered on virus genomic information and epidemiological data can be freely accessible at https://www.biosino.org/kgcov/, which is helpful to identify relevant knowledge and devising epidemic prevention and control policies in collaboration with disease control personnel. Measurement(s) Viral Epidemiology • genetic sequence variation analysis Technology Type(s) digital curation • Bioinformatics Sample Characteristic - Organism Severe acute respiratory syndrome coronavirus 2 [ABSTRACT FROM AUTHOR]

The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. [ABSTRACT FROM AUTHOR]

Transfer of SARS-CoV-2 from solids to fingers is one step in infection via contaminated solids, and the possibility of infection from this route has driven calls for increased frequency of handwashing during the COVID-19 pandemic. To analyze this route of infection, we measured the percentage of SARS-CoV-2 that was transferred from a solid to an artificial finger. A droplet of SARS-CoV-2 suspension (1 µL) was placed on a solid, and then artificial skin was briefly pressed against the solid with a light force (3 N). Transfer from a variety of solids was detected, and transfer from the non-porous solids, glass, stainless steel, and Teflon, was substantial when the droplet was still wet. The viral titer for the finger was 13–16% or 0.8–0.9 log less than for the input droplet. Transfer still occurred after the droplet evaporated, but was smaller, 3–9%. We found a lower level of transfer from porous solids but did not find a significant effect of solid wettability for non-porous solids. [ABSTRACT FROM AUTHOR]

Since the COVID-19 pandemic started in December 2019, there have been several reports of patients succumbing to neurological complications. Early reports were suggestive of a possibility, while by early 2020 it was clearly evident that although SARS-CoV-2 primarily attacks the respiratory system, the brain is one of the most affected organs post-recovery. Although it may be premature to comment on the long-term effects of COVID-19 in brain, some reliable predictions can be made based on the data currently available. Further, exploring the CNS connections of SARS-CoV-2 is of keen interest for neuroscience researchers. As soon as the virus enters the nasal region, it is exposed to the olfactory nervous system which is interlinked with the visual system, and hence we explore the mechanism of entry of this virus into CNS, including brain, olfactory and retinal nervous systems. In this review, we have thoroughly reviewed reports about both SARS-CoV-1 and SARS-CoV-2 with respect to their ability to breach the blood-brain and blood-retinal barriers. We have compiled different neurological conditions resulting from COVID-19 and looked into viral infections related to COVID-19 to understand how the virus may gain control of the olfactory and visual systems. Once the dust settles on the pandemic, it would be interesting to explore the extent of viral infection in the CNS. The long-term effects of this virus in the CNS are not yet known, and several scientific research papers evolving in this field will throw light on the same. [ABSTRACT FROM AUTHOR]

Intestinal epithelial cell damage caused by SARS-CoV-2 infection was thought to be associated with gastrointestinal symptoms and decreased fecal consistency. The association of the gastrointestinal symptoms with the COVID-19-mediated inflammatory response triggered by the gastrointestinal immune system was investigated in this paper. Intestinal inflammation marker fecal calprotectin along with serum calprotectin and other inflammatory markers were measured in COVID-19 cases with and without GI manifestations as well as healthy individuals. Analyses were performed to compare COVID-19 patient subgroups and healthy controls and examine the relationship between fecal and serum calprotectin levels with gastrointestinal symptoms and disease severity. COVID-19 patients (n = 70) were found to have markedly elevated median levels of fecal (124.3 vs. 25.0 µg/g; P < 0/0001) and serum calprotectin (3500 vs. 1060 ng/mL; P < 0/0001) compared with uninfected controls. Fecal and serum calprotectin levels were not significantly different between COVID-19 patients who displayed GI symptoms and those who did not. Compared with other acute phase markers, both fecal and serum calprotectin were superior in identifying COVID-19 patients who progressed to severe illness. Although the progression of COVID-19 disease is marked by an elevation of fecal and serum calprotectin, gastrointestinal symptoms or diarrhea were not correlated with calprotectin increase level. [ABSTRACT FROM AUTHOR]

Online social network is one of the most prominent media that holds information about society's epidemic problem. Due to privacy reasons, most of the users will not disclose their location. Detecting the location of the tweet users is required to track the geographic location of the spreading diseases. This work aims to detect the spreading location of the COVID-19 disease from the Twitter users and content discussed in the tweet. COVID-19 is a disease caused by the "novel coronavirus." About 80% of confirmed cases recover from the disease. However, one out of every six people who get COVID-19 can become seriously ill, stated by the World health organization. Inferring the user location for identifying the spreading location for the disease is a very challenging task. This paper proposes a new technique based on a hypergraph model to detect the Twitter user's locations based on the spreading disease. This model uses hypergraph with weighting factor technique to infer the spreading disease's spatial location. The accuracy of prediction can be improved when a massive volume of streaming data is analyzed. The Helly property of the hypergraph was applied to discard less potential words from the text analysis, which claims this work of unique nature. A weighting factor was introduced to calculate the score of each location for a particular user. The location of each user is predicted based on the one that possesses the highest weighting factor. The proposed framework has been evaluated and tested for various measures like precision, recall and F-measure. The promising results obtained have substantiated the claim for this work compared to the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

In this study, our aim is to explore the dynamics of COVID-19 or 2019-nCOV in Argentina considering the parameter values based on the real data of this virus from March 03, 2020 to March 29, 2021 which is a data range of more than one complete year. We propose a Atangana–Baleanu type fractional-order model and simulate it by using predictor–corrector (P-C) method. First we introduce the biological nature of this virus in theoretical way and then formulate a mathematical model to define its dynamics. We use a well-known effective optimization scheme based on the renowned trust-region-reflective (TRR) method to perform the model calibration. We have plotted the real cases of COVID-19 and compared our integer-order model with the simulated data along with the calculation of basic reproductive number. Concerning fractional-order simulations, first we prove the existence and uniqueness of solution and then write the solution along with the stability of the given P-C method. A number of graphs at various fractional-order values are simulated to predict the future dynamics of the virus in Argentina which is the main contribution of this paper. [ABSTRACT FROM AUTHOR]

The outbreak of severe acute respiratory syndrome coronavirus 2 is regarded as a highly contagious disease that has challenged the healthcare systems worldwide with confirmed cases approaching 12 million and more than 50,000 deaths. Considering the worldwide cases of novel coronavirus disease (COVID-19), it remains a pandemic and the vaccines and therapeutic agents have yet to be developed to stop the spread of this outbreak. Due to the unavailability of specific treatment for the COVID-19, it can be viewed that the risk of cluster infection will continue to be present within the intermittent and small-scale outbreaks. Though the COVID-19 has been identified as a communicable disease, the preventive measures and response policies in South Korea are effectively serving the purpose and gained the confidence to overcome the COVID-19 crisis. This paper includes the exploratory data analysis of COVID-19 cases in South Korea till July 8, 2020. South Korea has reported the lowest death rate with the majority of the deaths, associated with persons with underlying health conditions or elderly infected individuals. Currently the infected patients (total 989) remaining in South Korea are mild cases owing to its robust health care system and quarantine inspection procedures followed by the Ministry of Health and Welfare of South Korea to flatten the COVID-19 curve. Although the COVID-19 countermeasures taken by the South Korean government may not be conclusive or universal for all, but its exemplary approach to tackle COVID-19 can aid countries across the globe to strengthen their response system for the future outbreak of such an infectious disease. [ABSTRACT FROM AUTHOR]

Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic precipitated catastrophic job loss, unprecedented unemployment rates, and severe economic hardship in renter households. As a result, housing precarity and the risk of eviction increased and worsened during the pandemic, especially among people of color and low-income populations. This paper considers the implications of this eviction crisis for health and health inequity, and the need for eviction prevention policies during the pandemic. Eviction and housing displacement are particularly threatening to individual and public health during a pandemic. Eviction is likely to increase COVID-19 infection rates because it results in overcrowded living environments, doubling up, transiency, limited access to healthcare, and a decreased ability to comply with pandemic mitigation strategies (e.g., social distancing, self-quarantine, and hygiene practices). Indeed, recent studies suggest that eviction may increase the spread of COVID-19 and that the absence or lifting of eviction moratoria may be associated with an increased rate of COVID-19 infection and death. Eviction is also a driver of health inequity as historic trends, and recent data demonstrate that people of color are more likely to face eviction and associated comorbidities. Black people have had less confidence in their ability to pay rent and are dying at 2.1 times the rate of non-Hispanic Whites. Indigenous Americans and Hispanic/Latinx people face an infection rate almost 3 times the rate of non-Hispanic whites. Disproportionate rates of both COVID-19 and eviction in communities of color compound negative health effects make eviction prevention a critical intervention to address racial health inequity. In light of the undisputed connection between eviction and health outcomes, eviction prevention, through moratoria and other supportive measures, is a key component of pandemic control strategies to mitigate COVID-19 spread and death. [ABSTRACT FROM AUTHOR]

The issue of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has created enormous threat to global health. In an effort to contain the spread of COVID-19, a huge amount of disinfectants and antibiotics have been utilized on public health. Accordingly, the concentration of disinfectants and antibiotics is increasing rapidly in various environments, including wastewater, surface waters, soils and sediments. The aims of this study were to analyze the potential ecological environment impacts of disinfectants and antibiotics by summarizing their utilization, environmental occurrence, distribution and toxicity. The paper highlights the promoting effects of disinfectants and antibiotics on antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB). The scientific evidences indicate that the high concentration and high dose of disinfectants and antibiotics promote the evolution toward antimicrobial resistance through horizontal gene transformation and vertical gene transformation, which threaten human health. Further concerns should be focused more on the enrichment, bioaccumulation and biomagnification of disinfectants, antibiotics, antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB) in human bodies. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic and the measures taken to combat it led to severe constraints for various areas of life, including mobility. To study the effects of this disruptive situation on the mobility behaviour of entire subgroups, and how they shape their mobility in reaction to the special circumstances, can help to better understand, how people react to external changes. Methodology: Aim of the study presented in this article was to investigate to what extent, how and in what areas mobility behaviour has changed during the outbreak of SARS-CoV-2 in Germany. In addition, a focus was put on the comparison of federal states with and without lockdown in order to investigate a possible contribution of this measure to changes in mobility. We asked respondents via an online survey about their trip purposes and trip frequency, their choice of transport mode and the reasons for choosing it in the context of the COVID-19 crisis. For the analyses presented in this paper, we used the data of 4157survey participants (2512 without lockdown, 1645 with lockdown). Results: The data confirmed a profound impact on the mobility behaviour with a shift away from public transport and increases in car usage, walking and cycling. Comparisons of federal states with and without lockdown revealed only isolated differences. It seems that, even if the lockdown had some minor effects, its role in the observed behavioural changes was minimal. [ABSTRACT FROM AUTHOR]

According to the report presented by the World Health Organization, a new member of viruses, namely, coronavirus, shortly 2019-nCoV, which arised in Wuhan, China, on January 7, 2020, has been introduced to the literature. The main aim of this paper is investigating and finding the optimal values for better understanding the mathematical model of the transfer of 2019-nCoV from the reservoir to people. This model, named Bats-Hosts-Reservoir-People coronavirus (BHRPC) model, is based on bats as essential animal beings. By using a powerful numerical method we obtain simulations of its spreading under suitably chosen parameters. Whereas the obtained results show the effectiveness of the theoretical method considered for the governing system, the results also present much light on the dynamic behavior of the Bats-Hosts-Reservoir-People transmission network coronavirus model. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has both exposed and created deep rifts in society. It has thrust us into deep ethical thinking to help justify the difficult decisions many will be called upon to make and to protect from decisions that lack ethical underpinnings. This paper aims to highlight ethical issues in six different areas of life highlighting the enormity of the task we are faced with globally. In the context of COVID-19, we consider health inequity, dilemmas in triage and allocation of scarce resources, ethical issues associated with research, ethical considerations relating to tracing apps, and exit strategies such as immunity passports and COVID-19 vaccines. Finally, we consider environmental issues in light of COVID-19. The paper also offers some ethical reflection on these areas as many parts of the world contemplate the recovery phase. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the effectiveness of ensitrelvir, an oral antiviral, in reducing hospitalization risk in outpatients at high-risk for severe COVID-19 during the Omicron era. Methods: This was a retrospective study using a large Japanese health insurance claims database. It included high-risk outpatients for severe symptoms who received their first COVID-19 diagnosis between November 2022 and July 2023. The study included outpatients aged ≥ 18 years. The primary endpoint was all-cause hospitalization during the 4-week period from the date of outpatient diagnosis and medication, comparing the ensitrelvir group (n = 5177) and the no antiviral treatment group (n = 162,133). The risk ratio and risk difference were evaluated after adjusting patient background distribution by the inverse probability of treatment weight (IPTW) method. Secondary endpoints were incidence of respiratory and heart rate monitoring, oxygen therapy, ventilator use, intensive care admission, and all-cause death. Results: The risk ratio for all-cause hospitalization between the ensitrelvir group (n = 167,385) and the no antiviral treatment group (n = 167,310) after IPTW adjustment was 0.629 [95% confidence interval (CI) 0.420, 0.943]. The risk difference was − 0.291 [95% CI − 0.494, − 0.088]. The incidence of both respiratory and heart rate monitoring and oxygen therapy was lower in the ensitrelvir group. Ventilator use, intensive care admission, and all-cause death were difficult to assess because of the limited events. Conclusions: The incidence of all-cause hospitalization was significantly lower in the ensitrelvir group than in the no antiviral treatment group, suggesting ensitrelvir is an effective treatment in patients at risk of severe COVID-19. Plain Language Summary: COVID-19 still poses a risk for patients with serious health conditions and weakened immune systems, who are more likely to develop severe illness. Several studies have indicated that some oral antiviral medications might be effective in preventing severe disease. This study aimed to evaluate if ensitrelvir, an oral antiviral medication, can help prevent hospitalization in outpatients who are at risk of developing severe symptoms from the Omicron variant of the SARS-CoV-2 virus. The hospitalization rates of patients who received ensitrelvir was compared with those who did not receive any antiviral treatment, using medical records from a large health insurance database in Japan focused on outpatients who were at risk of severe symptoms and were diagnosed with COVID-19 between November 2022 and July 2023. Respiratory and heart rate monitoring, oxygen therapy, ventilator use, intensive care admission, and all-cause death were also evaluated. The study found that patients who received ensitrelvir had a lower risk of being hospitalized compared to those who did not receive any antiviral treatment. The ensitrelvir group also had lower rates of respiratory and heart rate monitoring and oxygen therapy. However, it was challenging to assess the effects on ventilator use, intensive care admission, and all-cause death due to the small number of events in the population under evaluation. Based on these findings, ensitrelvir appears to be an effective treatment for reducing the risk of hospitalization in patients at risk of severe COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has profoundly affected all aspects of our lives. Through real-time monitoring and rapid vaccine implementation, we succeeded in suppressing the spread of the disease and mitigating its consequences. Finally, conclusions can be summarized and drawn. Here, we use the example of Poland, which was seriously affected by the pandemic. Compared to other countries, Poland has not achieved impressive results in either testing or vaccination, which may explain its high mortality (case fatality rate, CFR 1.94%). Through retrospective analysis of data collected by the COVID-19 Data Portal Poland, we found significant regional differences in the number of tests performed, number of cases detected, number of COVID-19-related deaths, and vaccination rates. The Masovian, Greater Poland, and Pomeranian voivodeships, the country's leaders in vaccination, reported high case numbers but low death rates. In contrast, the voivodeships in the eastern and southern parts of Poland (Subcarpathian, Podlaskie, Lublin, Opole), which documented low vaccination levels and low case numbers, had higher COVID-19-related mortality rates. The strong negative correlation between the CFR and the percentage of the population that was vaccinated in Poland supports the validity of vaccination. To gain insight into virus evolution, we sequenced more than 500 genomes and analyzed nearly 80 thousand SARS-CoV-2 genome sequences deposited in GISAID by Polish diagnostic centers. We showed that the SARS-CoV-2 variant distribution over time in Poland reflected that in Europe. Haplotype network analysis allowed us to follow the virus transmission routes and identify potential superspreaders in each pandemic wave. [ABSTRACT FROM AUTHOR]

Maree-Cawthorn et al. ([3]) further examine some of the policy challenges posed by the COVID-19 pandemic that affect the future of sustainability. The SARS-COV-2 coronavirus now globally known as COVID-19 has generated a multi-faceted response since it was recognized by the World Health Organization as a pandemic in January 2020. Focus on the wildlife reservoir of the coronavirus as the ultimate source and wildlife trade as a major issue may be myopic because the issue of where the pandemic originated is only the opening act in the complex issues surrounding COVID-19. [Extracted from the article]

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research. [ABSTRACT FROM AUTHOR]

Background: With the spread and rise in the number of infections of COVID-19 globally, patients experienced neurologic manifestations such as headache and dizziness, along with other serious medical conditions such as stroke and seizures. Many reported cases highlighted headache to be one of the most common COVID-19-related neurological symptoms. With the evolution of the pandemic, broad studies are needed to better understand the mechanism of headache with COVID-19. The aim of this study is to recognize the epidemiology, characteristic and progression of headache among patients with COVID-19 in Lebanon. Methods: A retrospective cohort study was done to assess 439 patients for headache during the course of COVID-19. The Statistical Package for Social Sciences (SPSS) version 26 was used for descriptive analysis. Results: A confirmed COVID-19 Infection by PCR test was confirmed among 86% of the participants. Tension-like headache was the most common type of headache experienced by participants during COVID-19. Pain was mainly diffuse (48.7%), and compressing in nature (77.0%). Moreover, about 29% required a physician consultation and 7.7% had emergency visits. Partial relief by analgesics was reported in 54.9%, and mainly by paracetamol (64.6%). Those who reported having a history of primary headache before contracting COVID-19 were 46.7%. Among them, 36.7% patients reported a change in the type of headache and about 49.6% reported increased severity. Conclusion: Headache accompanied with COVID-19 is a common symptom during the early stages of illness. COVID-19 related headache was characterized as mainly involving the whole head region, pressuring, and partially relieved by analgesics. Thus, headache should raise early concern for the probability of being infected by COVID-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has had a broad impact on health services and health outcomes. During the pandemic, there were numerous reports of herpes zoster (HZ) in people with COVID-19 and in COVID-19 vaccine recipients. The aim of this review is to elucidate the global effects of the COVID-19 pandemic on HZ. It is postulated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection produces an immunosuppressive state that favours varicella zoster virus (VZV) reactivation. Three large cohort studies (a multinational study and studies from the USA and Spain) that excluded individuals vaccinated against HZ reported significantly increased risk of HZ following COVID-19 infection, especially in people aged ≥ 50 years. In contrast, a large study from Israel that did not consider HZ vaccination status reported no such increase. Cases of HZ following COVID-19 vaccination have been reported and may be the result of attenuated cell-mediated immunity. This phenomenon appears to vary by vaccine type. Some (but not all) large analyses have reported a significant positive relationship between receipt of mRNA vaccines for COVID-19 and development of HZ. These include analyses of health records databases in Israel and Hong Kong and of spontaneous case reports in the US Vaccine Adverse Event Reporting System (VAERS) database. Routine vaccinations, including shingles vaccine programmes, were disrupted by the COVID-19 pandemic. It is estimated that missed shingles vaccinations may have resulted in 63,117 avoidable HZ cases in the USA. Now that the World Health Organization has declared an end to the COVID-19 pandemic as a health emergency and routine vaccination services have resumed, there is a need to increase awareness of HZ and HZ vaccination. Graphical abstract available for this article. [ABSTRACT FROM AUTHOR]

Governments around the world have faced the challenge of how to respond to the recent outbreak of a novel coronavirus disease (COVID-19). Some have reacted by greatly restricting the freedom of citizens, while others have opted for less drastic policies. In this paper, I draw a parallel with vaccination ethics to conceptualize two distinct approaches to COVID-19 that I call altruistic and lockdown. Given that the individual measures necessary to limit the spread of the virus can in principle be achieved voluntarily as well as through enforcement, the question arises of how much freedom governments ought to give citizens to adopt the required measures. I argue that an altruistic approach is preferable on moral grounds: it preserves important citizen freedoms, avoids a number of potential injustices, and gives people a much-needed sense of meaning in precarious times. [ABSTRACT FROM AUTHOR]

I Drugs R D i B 21, b 9-27 (2021). https://doi.org/10.1007/s40268-020-00333-0 Butterworth, R.F. Potential for the Repurposing of Adamantane Antivirals for COVID-19. Indeed, I Drugs in R&D i has published a number of papers relevant to the COVID-19 pandemic that are free to access, including: Tegenge, M.A., Mahmood, I., Struble, E. I et al. i Dosing Considerations for Antibodies Against COVID-19. [Extracted from the article]

The COVID-19 outbreak caused by SARS-CoV-2 in late 2019 has spread rapidly across the world to form a global epidemic of respiratory infectious diseases. Increased investigations on diagnostic tools are currently implemented to assist rapid identification of the virus because mass and rapid diagnosis might be the best way to prevent the outbreak of the virus. This critical review discusses the detection principles, fabrication techniques, and applications on the rapid detection of SARS-CoV-2 with three categories: rapid nuclear acid augmentation test, rapid immunoassay test and biosensors. Special efforts were put on enhancement of nanomaterials on biosensors for rapid, sensitive, and low-cost diagnostics of SARS-CoV-2 virus. Future developments are suggested regarding potential candidates in hospitals, clinics and laboratories for control and prevention of large-scale epidemic. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID. The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients' CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities. [ABSTRACT FROM AUTHOR]

Introduction: The coronavirus disease 2019 (COVID-19) pandemic became superimposed on the pre-existing obesity and diabetes mellitus (DM) pandemics. Since COVID-19 infection alters the metabolic equilibrium, it may induce pathophysiologic mechanisms that potentiate new-onset DM, and we evaluated this issue. Method: A systematic review of the literature published from the 1 January 2020 until the 20 July 2023 was performed (PROSPERO registration number CRD42022341638). We included only full-text articles of both human clinical and randomized controlled trials published in English and enrolling adults (age > 18 years old) with ongoing or preceding COVID-19 in whom hyperglycemia was detected. The search was based on the following criteria: "(new-onset diabetes mellitus OR new-onset DM) AND (COVID-19) AND adults". Results: Articles on MEDLINE (n = 70) and the Web of Science database (n = 16) were included and analyzed by two researchers who selected 20 relevant articles. We found evidence of a bidirectional relationship between COVID-19 and DM. Conclusions: This link operates as a pathophysiological mechanism supported by epidemiological data and also by the clinical and biological findings obtained from the affected individuals. The COVID-19 pandemic raised the incidence of DM through different pathophysiological and psychosocial factors. [ABSTRACT FROM AUTHOR]

The impact of the novel coronavirus disease (COVID-19) continues unabated. Still, it seems that apart from contact and respiratory transmission, the design and development pattern of an area does echoes to be a contributing factor in virus spreadability. The present study considers land use and transportation system parameters under TOD mode of 16 BRT station provinces in Bhopal, India, and COVID-19 cases data were collected from April 2020 to August 2020. Further, the Pearson correlation and mediational analysis were employed to determine the relationship between TODness and COVID-19 spread cases. The bootstrapping method was used to evaluate the mediation effect and describe why and under what conditions they are related. The study shows that TODness and COVID-19 spread cases are positively correlated. The results show a considerable correlation at (p < 0.05) is 0.405 of the dispersed along with TODness of an area in the analysed 16 BRT station areas. In particular, dispersed demonstrated a high-level correlation of 0.681 with TOD areas, whereas a moderate correlation of 0.322 with non-TOD areas was mediated by diversity and the number of available transit service indicators. Diversity and availability of high-quality transit services effectively spread the virus, whereas population density and public transport mediation effects are insignificant. Outcomes from this study may help government authorities and policymakers devise a strategy and adopt preventive measures in subsequent waves of the pandemic. [ABSTRACT FROM AUTHOR]

It is imperative that resources are channelled towards programs that are efficient and cost effective in combating the spread of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study proposed and analyzed control strategies for that purpose. We developed a mathematical disease model within an optimal control framework that allows us to investigate the best approach for curbing COVID-19 epidemic. We address the following research question: what is the role of community compliance as a measure for COVID-19 control? Analyzing the impact of community compliance of recommended guidelines by health authorities—examples, social distancing, face mask use, and sanitizing—coupled with efforts by health authorities in areas of vaccine provision and effective quarantine—showed that the best intervention in addition to implementing vaccination programs and effective quarantine measures, is the active incorporation of individuals' collective behaviours, and that resources should also be directed towards community campaigns on the importance of face mask use, social distancing, and frequent sanitizing, and any other collective activities. We also demonstrated that collective behavioral response of individuals influences the disease dynamics; implying that recommended health policy should be contextualized. [ABSTRACT FROM AUTHOR]

Demand for mass surveillance during peak times of the SARS-CoV-2 pandemic caused high workload for clinical laboratories. Efficient and cost conserving testing designs by means of group testing can substantially reduce resources during possible future emergency situations. The novel hypercube algorithm proposed by Mutesa et al. 2021 published in Nature provides methodological proof of concept and points out the applicability to epidemiological testing. In this work, the algorithm is explored and expanded for settings with high group prevalence. Numerical studies investigate the limits of the adapted hypercube methodology, allowing to optimize pooling designs for specific requirements (i.e. number of samples and group prevalence). Hyperparameter optimization is performed to maximize test-reduction. Standard deviation is examined to investigate resilience and precision. Moreover, empirical validation was performed by elaborately pooling SARS-CoV-2 virus samples according to numerically optimized pooling designs. Laboratory experiments with SARS-CoV-2 sample groups, ranging from 50 to 200 items, characterized by group prevalence up to 10%, are successfully processed and analysed. Test-reductions from 50 to 72.5% were achieved in the experimental setups when compared to individual testing. Higher theoretical test-reduction is possible, depending on the number of samples and the group prevalence, indicated by simulation results. [ABSTRACT FROM AUTHOR]

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period. [ABSTRACT FROM AUTHOR]

Ivermectin, an antiparasitic drug, has been repurposed for COVID-19 treatment during the SARS-CoV-2 pandemic. Although its antiviral efficacy was confirmed early in vitro and in preclinical studies, its clinical efficacy remained ambiguous. Our purpose was to assess the efficacy of ivermectin in terms of time to viral clearance based on the meta-analysis of available clinical trials at the closing date of the data search period, one year after the start of the pandemic. This meta-analysis was reported by following the PRISMA guidelines and by using the PICO format for formulating the question. The study protocol was registered on PROSPERO. Embase, MEDLINE (via PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), bioRvix, and medRvix were searched for human studies of patients receiving ivermectin therapy with control groups. No language or publication status restrictions were applied. The search ended on 1/31/2021 exactly one year after WHO declared the public health emergency on novel coronavirus. The meta-analysis of three trials involving 382 patients revealed that the mean time to viral clearance was 5.74 days shorter in case of ivermectin treatment compared to the control groups [WMD = −5.74, 95% CI (−11.1, −0.39), p = 0.036]. Ivermectin has significantly reduced the time to viral clearance in mild to moderate COVID-19 diseases compared to control groups. However, more eligible studies are needed for analysis to increase the quality of evidence of ivermectin use in COVID-19. [ABSTRACT FROM AUTHOR]

Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19) and is influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the genetically diverse Collaborative Cross (CC) founder strains crossed to human ACE2 transgenic mice (K18-hACE2) that confers susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 resulted in a spectrum of survival, viral replication kinetics, and immune profiles. Importantly, in contrast to the K18-hACE2 model, early type I interferon (IFN-I) and regulated proinflammatory responses were required for control of SARS-CoV-2 replication in PWK x K18-hACE2 mice that were highly resistant to disease. Thus, virus dynamics and inflammation observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding anti-coronavirus immunity. Dynamics of type I interferon (IFN) following infection with SARS-CoV-2 are critical in determining disease severity in humans but have been difficult to model in mice. Here, infection of genetically diverse mice reveals how delayed or immediate IFN signaling coordinates antiviral immunity. [ABSTRACT FROM AUTHOR]

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is associated with increased pregnancy complications. Despite effective vaccination strategies for the general population, the evidence on the safety and efficacy of Coronavirus disease 2019 (COVID-19) vaccinations in pregnancy is limited due to a lack of well-powered studies. The present study compares the maternal, neonatal, and immunological outcomes between vaccinated pregnant and unvaccinated pregnant women using a systematic review and meta-analysis approach. We included 37 studies with a total of 141,107 pregnant women (36.8% vaccinated) spread across all outcomes. Our evidence indicates a higher rate of cesarean section in the 1898 vaccinated pregnant women compared to the 6180 women who did not receive vaccination (OR = 1.20, CI = (1.05, 1.38), P = 0.007, I2 = 45%). Regarding immunological outcomes, the risk of SARS-CoV-2 infection during pregnancy or postpartum was significantly reduced in 6820 vaccinated pregnant women compared to 17,010 unvaccinated pregnant women (OR = 0.25, CI = 0.13–0.48, P < 0.0001, I2 = 61%), as evident from qualitative assessment indicating significantly higher postpartum antibody titers compared to that observed in both unvaccinated mothers and mothers who have recently recovered from a SARS-CoV-2 infection. Our analysis represents high quality evidence showing that COVID-19 vaccination effectively raises antibody titers against SARS-CoV-2. This may confer protection against infection during pregnancy and the postpartum period. In addition to being protective against SARS-CoV-2, the vaccine was associated with decreased odds of preterm delivery. Furthermore, COVID-19 vaccination may also be associated with higher odds of cesarean section. [ABSTRACT FROM AUTHOR]

Prophylactic vaccines for SARS-CoV-2 have lowered the incidence of severe COVID-19, but emergence of viral variants that are antigenically distinct from the vaccine strains are of concern and additional, broadly acting preventive approaches are desirable. Here, we report on a glycolipid termed 7DW8-5 that exploits the host innate immune system to enable rapid control of viral infections in vivo. This glycolipid binds to CD1d on antigen-presenting cells and thereby stimulates NKT cells to release a cascade of cytokines and chemokines. The intranasal administration of 7DW8-5 prior to virus exposure significantly blocked infection by three different authentic variants of SARS-CoV-2, as well as by respiratory syncytial virus and influenza virus, in mice or hamsters. We also found that this protective antiviral effect is both host-directed and mechanism-specific, requiring both the CD1d molecule and interferon- γ . A chemical compound like 7DW8-5 that is easy to administer and cheap to manufacture may be useful not only in slowing the spread of COVID-19 but also in responding to future pandemics long before vaccines or drugs are developed. 7DW8-5 is a glycolipid that binds CD1d and stimulates invariant natural killer T (iNKT) cells. Here the authors show that 7DW8-5, when administered intranasally, provides prophylactic anti-viral effects against influenza, RSV, and SARS-CoV-2 in mice or hamsters, and that this effect is mediated by iNKT cells and IFN-γ. [ABSTRACT FROM AUTHOR]

COVID-19 survivors often suffer from post-acute sequelae of SARS-CoV-2 infection (PASC). Current evidence suggests dysregulated alveolar regeneration as a possible explanation for respiratory PASC, which deserves further investigation in a suitable animal model. This study investigates morphological, phenotypical and transcriptomic features of alveolar regeneration in SARS-CoV-2 infected Syrian golden hamsters. We demonstrate that CK8+ alveolar differentiation intermediate (ADI) cells occur following SARS-CoV-2-induced diffuse alveolar damage. A subset of ADI cells shows nuclear accumulation of TP53 at 6- and 14-days post infection (dpi), indicating a prolonged arrest in the ADI state. Transcriptome data show high module scores for pathways involved in cell senescence, epithelial-mesenchymal transition, and angiogenesis in cell clusters with high ADI gene expression. Moreover, we show that multipotent CK14+ airway basal cell progenitors migrate out of terminal bronchioles, aiding alveolar regeneration. At 14 dpi, ADI cells, peribronchiolar proliferates, M2-macrophages, and sub-pleural fibrosis are observed, indicating incomplete alveolar restoration. The results demonstrate that the hamster model reliably phenocopies indicators of a dysregulated alveolar regeneration of COVID-19 patients. The results provide important information on a translational COVID-19 model, which is crucial for its application in future research addressing pathomechanisms of PASC and in testing of prophylactic and therapeutic approaches for this syndrome. Heydemann et al. investigate alveolar regeneration mechanisms after SARS-CoV-2 infection in the hamster model, offering insight into the pathomechanisms of post-acute sequelae of SARS-CoV-2 infection (PASC). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has inflicted major economic and health burdens across the world. On the other hand, the potential airborne transmission of SARS-COV-2 via air can deeply undermine the effectiveness of countermeasures against spreading the disease. Therefore, there is an intense focus to look for ways to mitigate the COVID-19 spread within various indoor settings. This work systematically reviewed articles regarding airborne transmission of SARS-COV2 in various indoor settings since the onset of the pandemic. The systematic search was performed in Scopus, Web of Science, and PubMed databases and has returned 19 original articles carefully screened with regard to inclusion and exclusion criteria. The results showed that the facilities, such as dormitories and classrooms, received the most attention followed by office buildings, healthcare facilities, residential buildings, and other potential enclosed spaces such as a metro wagon. Besides, the majority of the studies were conducted experimentally while other studies were done using computer simulations. United States (n = 5), Spain (n = 4) and China (n = 3) were the top three countries based on the number of performed research. Ventilation rate was the most influential parameter in controlling the infection spread. CO2 was the primary reference for viral spread in the buildings. The use of natural ventilation or a combination of mechanical and natural ventilations was found to be highly effective in the studies. The current work helps in furthering research on effective interventions to improve indoor air quality and control the spread of COVID-19 and other respiratory diseases. [ABSTRACT FROM AUTHOR]

Keywords: SARS-CoV-2; COVID-19; Audiovestibular disorders; Vertigo; Dizziness; Audiovestibular evaluation EN SARS-CoV-2 COVID-19 Audiovestibular disorders Vertigo Dizziness Audiovestibular evaluation 1311 1314 4 06/05/23 20230601 NES 230601 Dear Editor, We read with interest the article by Motawea et al. reporting the case of a sudden vertigo in a 60-year-old woman with a confirmed Coronavirus-Disease-19 (COVID-19) [[1]]. SARS-CoV-2, COVID-19, Audiovestibular disorders, Vertigo, Dizziness, Audiovestibular evaluation The authors concluded that Severe-Acute-Respiratory-Syndrome-Coronavirus-2 (SARS-COV-2) may lead to vestibular neuritis (VN), but more well-designed observational studies with a larger sample size are needed to establish a definite association between COVID-19 and vertigo. [Extracted from the article]