Search

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients. [ABSTRACT FROM AUTHOR]

To control the SARS-CoV-2 pandemic, healthcare systems have focused on ramping up their capacity for epidemiological surveillance through viral whole genome sequencing. In this paper, we tested the performance of two protocols of SARS-CoV-2 nucleic acid enrichment, an amplicon enrichment using different versions of the ARTIC primer panel and a hybrid-capture method using KAPA RNA Hypercap. We focused on the challenge of the Omicron variant sequencing, the advantages of automated library preparation and the influence of the bioinformatic analysis in the final consensus sequence. All 94 samples were sequenced using Illumina iSeq 100 and analysed with two bioinformatic pipelines: a custom-made pipeline and an Illumina-owned pipeline. We were unsuccessful in sequencing six samples using the capture enrichment due to low reads. On the other hand, amplicon dropout and mispriming caused the loss of mutation G21987A and the erroneous addition of mutation T15521A respectively using amplicon enrichment. Overall, we found high sequence agreement regardless of method of enrichment, bioinformatic pipeline or the use of automation for library preparation in eight different SARS-CoV-2 variants. Automation and the use of a simple app for bioinformatic analysis can simplify the genotyping process, making it available for more diagnostic facilities and increasing global vigilance. [ABSTRACT FROM AUTHOR]

Background: Data on SARS-CoV-2 infection in pregnancy and infancy has accumulated throughout the course of the pandemic, though evidence regarding asymptomatic SARS-CoV-2 infection and adverse birth outcomes are scarce. Limited information is available from countries in sub-Saharan Africa (SSA). The pregnant woman and infant COVID in Africa study (PeriCOVID Africa) is a South-South-North partnership involving hospitals and health centres in five countries: Malawi, Uganda, Mozambique, The Gambia, and Kenya. The study leveraged data from three ongoing prospective cohort studies: Preparing for Group B Streptococcal Vaccines (GBS PREPARE), SARS-CoV-2 infection and COVID-19 in women and their infants in Kampala and Mukono (COMAC) and Pregnancy Care Integrating Translational Science Everywhere (PRECISE). In this paper we describe the seroepidemiology of SARS-CoV-2 infection in pregnant women enrolled in sites in Uganda and Malawi, and the impact of SARS-CoV-2 infection on pregnancy and infant outcomes. Outcome: Seroprevalence of SARS-CoV-2 antibodies in maternal blood, reported as the proportion of seropositive women by study site and wave of COVID-19 within each country. Methods: The PeriCOVID study was a prospective mother-infant cohort study that recruited pregnant women at any gestation antenatally or on the day of delivery. Maternal and cord blood samples were tested for SARS-CoV-2 antibodies using Wantai and Euroimmune ELISA. In periCOVID Uganda and Malawi nose and throat swabs for SARS-Cov-2 RT-PCR were obtained. Results: In total, 1379 women were enrolled, giving birth to 1387 infants. Overall, 63% of pregnant women had a SARS-CoV-2 positive serology. Over subsequent waves (delta and omicron), in the absence of vaccination, seropositivity rose from 20% to over 80%. The placental transfer GMR was 1.7, indicating active placental transfer of anti-spike IgG. There was no association between SARS-CoV-2 antibody positivity and adverse pregnancy or infancy outcomes. [ABSTRACT FROM AUTHOR]

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters—two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus (SARS-CoV-2 / COVID-19), with a point of origin in Wuhan, China, has spread rapidly all over the world. It turned into a raging pandemic wrecking havoc on health care facilities, world economy and affecting everyone's life to date. With every new variant, rate of transmission, spread of infections and the number of cases continues to rise at an international level and scale. There are limited reliable researches that study microdroplets spread and transmissions from human sneeze or cough in the airborne space. In this paper, we propose an intelligent technique to visualize, detect, measure the distance of spread in a real-world settings of microdroplet transmissions in airborne space, called "COVNET45". In this paper, we investigate the microdroplet transmission and validate the measurements accuracy compared to published researches, by examining several microscopic and visual images taken to investigate the novel coronavirus (SARS-CoV-2 / COVID-19). The ultimate contribution is to calculate the spread of the microdroplets, measure it precisely and provide a graphical presentation. Additionally, the work employs machine learning and five algorithms for image optimization, detection and measurement. [ABSTRACT FROM AUTHOR]

Since the emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), its genetic and geographical origins remain unclear, resulting in suspicions about its natural origin. In one of our previous studies, we reported the presence of a furin cleavage site RRAR in the junction region between S1 and S2 subunits of the spike protein, which was discovered as the first crucial clue for the origin tracing of SARS-CoV-2. In the present study, we conducted an integrative analysis of new genome data from bat Sarbecovirus strains reported after the COVID-19 outbreak. The primary results included the identification of BANAL-20-52, Rp22DB159, and S18CXBatR24 as three close relatives of SARS-CoV-2 and the successful detection of seven out of nine key genomic features (designated as RC0-7 and ORF8) observed in wild types of SARS-CoV-2 in the three close relatives from Laos, Vietnam, and Yunnan province of China, respectively. The most significant contribution of the present study lies in the detection of RC1 in wild genotype in a bat Sarbecovirus population BANAL-20-52 belonging to. Encoding a segment of the NSP3 protein, RC1 was discovered as the second crucial clue for the origin tracing of SARS-CoV-2. Although RC0, encoding the junction furin cleavage site, remains undetected outside of the SARS-CoV-2 genome, Feuang of Laos is the sole place where eight of the nine wild-type features (RC1-7 and ORF8) have been detected. [ABSTRACT FROM AUTHOR]

Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model's inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic. [ABSTRACT FROM AUTHOR]

In March 2020, the World Health Organisation named the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2), which causes corona virus disease 2019 (COVID –19), as a pandemic. Pregnant women were considered at increased risk of developing severe COVID-19 after viral infection. In response maternity services reduced face-to-face consultations with high-risk pregnant women by supplying blood pressure monitors for supported self-monitoring. This paper explores the experiences of patients and clinicians of the rapid roll-out of supported self-monitoring programme in Scotland during the first and second wave of the COVID-19 pandemic. We conducted semi-structured telephone interviews with high-risk women and healthcare professionals who were using supported self-monitoring of blood pressure (BP) In four case studies during the COVID-19 pandemic. 20 women, 15 midwives and 4 obstetricians took part in the interviews. Interviews with healthcare professionals showed that while implementation occurred at pace and at scale across the National Health Service (NHS) in Scotland, implementation differed locally, resulting in mixed experiences. Study Participants observed several barriers and facilitators to implementation. Women value the simplicity of use and convenience of the digital communications platforms while health professionals were more interested in their impact on reducing workload for both women and health professionals largely found self-monitoring acceptable, with only a few exceptions. These results show that rapid change can occur in the NHS at a national level when there is a shared motivation. While self-monitoring is acceptable to most women, decisions regarding self-monitoring should be made jointly and on an individual basis. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically. These literary items encompass a varied set of topics, ranging from vaccination to protective equipment efficacy as well as lockdown policy evaluations. As a result, the development of automatic methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in on its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis, to investigate topics under their surface. More specifically, LDA2Net exploits the frequencies of consecutive words pairs (i.e. bigram) to build those network structures underlying the hidden topics extracted from large volumes of text by Latent Dirichlet Allocation (LDA). Results are promising and suggest that the topic model efficacy is magnified by the network-based representation. In particular, such enrichment is noticeable when it comes to displaying and exploring the topics at different levels of granularity. [ABSTRACT FROM AUTHOR]

This paper proposes a compartment model (SVEIHRM model) based on a system of ordinary differential equations to simulate the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Emergence of mutant viruses gave rise to multiple peaks in the number of confirmed cases. Vaccine developers and WHO suggest individuals to receive multiple vaccinations (the primary and the secondary vaccinations and booster shots) to mitigate transmission of COVID-19. Taking this into account, we include compartments for multiple vaccinations and mutant viruses of COVID-19 in the model. In particular, our model considers breakthrough infection according to the antibody formation rate following multiple vaccinations. We obtain the effective reproduction numbers of the original virus, the Delta, and the Omicron variants by fitting this model to data in Korea. Additionally, we provide various simulations adjusting the daily vaccination rate and the timing of vaccination to investigate the effects of these two vaccine-related measures on the number of infected individuals. We also show that starting vaccinations early is the key to reduce the number of infected individuals. Delaying the start date requires increasing substantially the rate of vaccination to achieve similar target results. In the sensitivity analysis on the vaccination rate of Korean data, it is shown that a 10% increase (decrease) in vaccination rates can reduce (increase) the number of confirmed cases by 35.22% (82.82%), respectively. [ABSTRACT FROM AUTHOR]

In 2020, COVID-19 became a global pandemic. Older people are less resistant to the novel coronavirus. In order to ensure the health of the elderly population, the governments of five Central Asian countries should provide home medical services for the elderly or provide "green channel" to medical services. This "green channel" means providing a special service and treatment for the elderly in the hospital to ensure that they can safely and easily access the medical services they need. In order to study the application scope of various modes, this article constructs three modes of differential game: no special care, home medical care and "green channel". And the equilibrium results are compared and analyzed. Research shows that when the additional medical costs associated with house calls or "green channel" gradually increase, the social benefits to both the elderly and the government gradually decrease, and eventually it is less than the social benefits under the no-special care model. The greater the credibility of the government under the "green channel" service model, the greater the social benefits of the government. However, the greater the credibility of the government under the home medical service model, the smaller the social benefits of the government. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of the novel coronavirus disease of 2019 (COVID-19) has led to huge disruptions in the medical field and society. The significance of training and education for experts has been increasingly acknowledged in Japan, where the number of infectious disease (ID) specialists is reportedly insufficient. In this paper, we report the results of a web-based survey that was conducted to reveal the ways in which the COVID-19 pandemic has influenced medical students' awareness of ID specialists and future career choices. Method: This cross-sectional descriptive study was conducted in March 2021 and targeted 717 medical students belonging to Medical School of Okayama University, Japan. The questionnaire consisted of four questions meant to assess students' knowledge and future intentions of becoming ID specialists. Results: We obtained 328 eligible questionnaires (response rate: 45.7%). Of 227 (69.2%) students who were aware of ID specialists, 99 (43.6%) answered that they came to know about them only after the pandemic, 12 (3.7%) answered that their interest in being an ID specialist arose during the pandemic, while 36 (11.0%) responded that they would rather not become ID specialists. At the time of the survey, 5 students (1.5%) were aiming to become ID specialists. Conclusion: We observed a very low rate of interest to be an ID specialist among medical students. The experience of the pandemic does not seem to have influenced Japanese medical students to choose ID as a specialty for their careers. Continuous efforts to increase the number of ID specialists are necessary in Japan as a countermeasure against the coming pandemic. [ABSTRACT FROM AUTHOR]

The pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took the world by surprise. Following the first outbreak of COVID-19 in December 2019, several models have been developed to study and understand its transmission dynamics. Although the spread of COVID-19 is being slowed down by vaccination and other interventions, there is still a need to have a clear understanding of the evolution of the pandemic across countries, states and communities. To this end, there is a need to have a clearer picture of the initial spread of the disease in different regions. In this project, we used a simple SEIR model and a Bayesian inference framework to estimate the basic reproduction number of COVID-19 across Africa. Our estimates vary between 1.98 (Sudan) and 9.66 (Mauritius), with a median of 3.67 (90% CrI: 3.31–4.12). The estimates provided in this paper will help to inform COVID-19 modeling in the respective countries/regions. [ABSTRACT FROM AUTHOR]

This paper proposes some high-ordered integer-valued auto-regressive time series process of order p (INAR(p)) with Zero-Inflated and Poisson-mixtures innovation distributions, wherein the predictor functions in these mentioned distributions allow for covariate specification, in particular, time-dependent covariates. The proposed time series structures are tested suitable to model the SARs-CoV-2 series in Mauritius which demonstrates excess zeros and hence significant over-dispersion with non-stationary trend. In addition, the INAR models allow the assessment of possible causes of COVID-19 in Mauritius. The results illustrate that the event of Vaccination and COVID-19 Stringency index are the most influential factors that can reduce the locally acquired COVID-19 cases and ultimately, the associated death cases. Moreover, the INAR(7) with Zero-inflated Negative Binomial innovations provides the best fitting and reliable Root Mean Square Errors, based on some short term forecasts. Undeniably, these information will hugely be useful to Mauritian authorities for implementation of comprehensive policies. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has caused widespread disruptions including to health services. In the early response to the pandemic many countries restricted population movements and some health services were suspended or limited. In late 2020 and early 2021 some countries re-imposed restrictions. Health authorities need to balance the potential harms of additional SARS-CoV-2 transmission due to contacts associated with health services against the benefits of those services, including fewer new HIV infections and deaths. This paper examines these trade-offs for select HIV services. Methods: We used four HIV simulation models (Goals, HIV Synthesis, Optima HIV and EMOD) to estimate the benefits of continuing HIV services in terms of fewer new HIV infections and deaths. We used three COVID-19 transmission models (Covasim, Cooper/Smith and a simple contact model) to estimate the additional deaths due to SARS-CoV-2 transmission among health workers and clients. We examined four HIV services: voluntary medical male circumcision, HIV diagnostic testing, viral load testing and programs to prevent mother-to-child transmission. We compared COVID-19 deaths in 2020 and 2021 with HIV deaths occurring now and over the next 50 years discounted to present value. The models were applied to countries with a range of HIV and COVID-19 epidemics. Results: Maintaining these HIV services could lead to additional COVID-19 deaths of 0.002 to 0.15 per 10,000 clients. HIV-related deaths averted are estimated to be much larger, 19–146 discounted deaths per 10,000 clients. Discussion: While there is some additional short-term risk of SARS-CoV-2 transmission associated with providing HIV services, the risk of additional COVID-19 deaths is at least 100 times less than the HIV deaths averted by those services. Ministries of Health need to take into account many factors in deciding when and how to offer essential health services during the COVID-19 pandemic. This work shows that the benefits of continuing key HIV services are far larger than the risks of additional SARS-CoV-2 transmission. [ABSTRACT FROM AUTHOR]

Objective: The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods: Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results: Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions: The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic progresses, widespread community transmission of SARS-CoV-2 has ushered in a volatile era of viral immune evasion rather than the much-heralded stability of "endemicity" or "herd immunity." At this point, an array of viral strains has rendered essentially all monoclonal antibody therapeutics obsolete and strongly undermined the impact of vaccinal immunity on SARS-CoV-2 transmission. In this work, we demonstrate that antibody escape resulting in evasion of pre-existing immunity is highly evolutionarily favored and likely to cause waves of short-term transmission. In the long-term, invading strains that induce weak cross-immunity against pre-existing strains may co-circulate with those pre-existing strains. This would result in the formation of serotypes that increase disease burden, complicate SARS-CoV-2 control, and raise the potential for increases in viral virulence. Less durable immunity does not drive positive selection as a trait, but such strains may transmit at high levels if they establish. Overall, our results draw attention to the importance of inter-strain cross-immunity as a driver of transmission trends and the importance of early immune evasion data to predict the trajectory of the pandemic. [ABSTRACT FROM AUTHOR]

The unfolding of the COVID-19 pandemic has been very difficult to predict using mathematical models for infectious diseases. While it has been demonstrated that variations in susceptibility have a damping effect on key quantities such as the incidence peak, the herd-immunity threshold and the final size of the pandemic, this complex phenomenon is almost impossible to measure or quantify, and it remains unclear how to incorporate it for modeling and prediction. In this work we show that, from a modeling perspective, variability in susceptibility on an individual level is equivalent with a fraction θ of the population having an "artificial" sterilizing immunity. We also derive novel formulas for the herd-immunity threshold and the final size of the pandemic, and show that these values are substantially lower than predicted by the classical formulas, in the presence of variable susceptibility. In the particular case of SARS-CoV-2, there is by now undoubtedly variable susceptibility due to waning immunity from both vaccines and previous infections, and our findings may be used to greatly simplify models. If such variations were also present prior to the first wave, as indicated by a number of studies, these findings can help explain why the magnitude of the initial waves of SARS-CoV-2 was relatively low, compared to what one may have expected based on standard models. [ABSTRACT FROM AUTHOR]

The global impact of the SARS-CoV-2 pandemic has underscored the need for a deeper understanding of viral evolution to anticipate new viruses or variants. Genetic recombination is a fundamental mechanism in viral evolution, yet it remains poorly understood. In this study, we conducted a comprehensive research on the genetic regions associated with genetic recombination features in SARS-CoV-2. With this aim, we implemented a two-phase transfer learning approach using genomic spectrograms of complete SARS-CoV-2 sequences. In the first phase, we utilized a pre-trained VGG-16 model with genomic spectrograms of HIV-1, and in the second phase, we applied HIV-1 VGG-16 model to SARS-CoV-2 spectrograms. The identification of key recombination hot zones was achieved using the Grad-CAM interpretability tool, and the results were analyzed by mathematical and image processing techniques. Our findings unequivocally identify the SARS-CoV-2 Spike protein (S protein) as the pivotal region in the genetic recombination feature. For non-recombinant sequences, the relevant frequencies clustered around 1/6 and 1/12. In recombinant sequences, the sharp prominence of the main hot zone in the Spike protein prominently indicated a frequency of 1/6. These findings suggest that in the arithmetic series, every 6 nucleotides (two triplets) in S may encode crucial information, potentially concealing essential details about viral characteristics, in this case, recombinant feature of a SARS-CoV-2 genetic sequence. This insight further underscores the potential presence of multifaceted information within the genome, including mathematical signatures that define an organism's unique attributes. [ABSTRACT FROM AUTHOR]

While the early effects of the COVID-19 pandemic on the United States labor market are well-established, less is known about the long-term impact of SARS-CoV-2 infection and Long COVID on employment. To address this gap, we analyzed self-reported data from a prospective, national cohort study to estimate the effects of SARS-CoV-2 symptoms at three months post-infection on missed workdays and return to work. The analysis included 2,939 adults in the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) study who tested positive for their initial SARS-CoV-2 infection at the time of enrollment, were employed before the pandemic, and completed a baseline and three-month electronic survey. At three months post-infection, 40.8% of participants reported at least one SARS-CoV-2 symptom and 9.6% of participants reported five or more SARS-CoV-2 symptoms. When asked about missed work due to their SARS-CoV-2 infection at three months, 7.2% of participants reported missing ≥10 workdays and 13.9% of participants reported not returning to work since their infection. At three months, participants with ≥5 symptoms had a higher adjusted odds ratio of missing ≥10 workdays (2.96, 95% CI 1.81–4.83) and not returning to work (2.44, 95% CI 1.58–3.76) compared to those with no symptoms. Prolonged SARS-CoV-2 symptoms were common, affecting 4-in-10 participants at three-months post-infection, and were associated with increased odds of work loss, most pronounced among adults with ≥5 symptoms at three months. Despite the end of the federal Public Health Emergency for COVID-19 and efforts to "return to normal", policymakers must consider the clinical and economic implications of the COVID-19 pandemic on people's employment status and work absenteeism, particularly as data characterizing the numerous health and well-being impacts of Long COVID continue to emerge. Improved understanding of risk factors for lost work time may guide efforts to support people in returning to work. [ABSTRACT FROM AUTHOR]

Background: The coronavirus Disease 2019 (COVID-19) pandemic has brought about unique challenges in healthcare and nutrition, particularly for people living with HIV (PLHIV). Understanding their dietary patterns and nutritional status is crucial for developing targeted interventions and improving health outcomes. Therefore, this study assessed the dietary diversity and nutritional status of PLHIV during the COVID-19 era. Methods: We adopted a facility-based cross-sectional study design to enroll 220 PLHIV from two hospitals in the Central Region of Ghana. Dietary intakes were assessed using 24-hour recall. Anthropometric and body composition data were collected with a stadiometer and a body composition monitor. Dietary diversity was evaluated using the FAO's Individual Dietary Diversity Score (IDDS). Data analysis was conducted with SPSS version 20. Significance level was set p-value less than 0.05. Results: A significant proportion (33.2%) of PLHIV had low dietary diversity, with the majority (55.5%) categorized as needing dietary improvement. Approximately 2 out of every 10 of the participants were identified as underweight. Participants aged 40 to 59 years were more likely to exhibit higher dietary diversity (adjusted odds ratio (AOR) = 1.966, 95% Confidence Interval (CI): 1.045–4.987). Participants who consumed meals at least three times daily were more likely to have a high IDDS (AOR = 1.641, 95% CI: 1.221, 8.879). Employed participants (public sector and private sector) were also more likely to have a high IDDS compared to unemployed participants (AOR = 1.448, 95% CI: 1.028–3.042; AOR = 1.165, 95% CI: 1.030–9.329, respectively). Factors associated with undernutrition included being female (AOR = 1.829, 95% CI: 1.294, 3.872) and first-line antiretroviral therapy ART (AOR = 1.683, 95% CI: 1.282–2.424). Conclusion: The study emphasizes the need for nutritional interventions for PLHIV, particularly during crises. It advocates for a policy collaboration to address food insecurity and promote resilient health outcomes. [ABSTRACT FROM AUTHOR]

Robust testing capacity was necessary for public health agencies to respond to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 19 (COVID-19) pandemic. As the nation faced the need for robust testing capacity, it became necessary to use all possible resources. In many cases, veterinary diagnostic laboratories rose to meet this demand because these facilities routinely perform high throughput diagnostic testing of large animal populations and are typically familiar with pathogens of high pandemic concern. In this study, we evaluated the impact of veterinary diagnostic laboratories in the United States on SARS-CoV-2 testing. Results of surveys, semi-structured interviews, and analysis of publicly available information showed that veterinary diagnostic laboratories had a substantial impact on human health through population-level testing in the COVID-19 response, supporting timely and informed public health interventions. This success was not without significant hurdles, as many participating veterinary diagnostic laboratories experienced restriction in their response due to difficulties obtaining the Clinical Laboratory Improvement Amendments (CLIA) certification required to conduct human diagnostic testing. Our results point out the importance of reducing hurdles before the next major public health emergency to enhance access to testing resources overall and to ultimately improve population health. [ABSTRACT FROM AUTHOR]

There has been a lot of discussion about the role of schools in the transmission of severe acute respiratory coronavirus 2 (SARS-CoV-2) during the coronavirus 2019 (COVID-19) pandemic, where many countries responded with school closures in 2020. Reopening of primary schools in the Netherlands in February 2021 was sustained by various non-pharmaceutical interventions (NPIs) following national recommendations. Our study attempted to assess the degree of regional implementation and effectiveness of these NPIs in South Limburg, Netherlands. We approached 150 primary schools with a structured questionnaire containing items on the implementation of NPIs, including items on ventilation. Based on our registry of cases, we determined the number of COVID-19 cases linked to each school, classifying cases by their source of transmission. We calculated a crude secondary attack rate by dividing the number of cases of within-school transmission by the total number of children and staff members. Two-sample proportion tests were performed to compare these rates between schools stratified by the presence of a ventilation system and mask mandates for staff members. A total of 69 schools responded. Most implemented NPIs were aimed at students, except for masking mandates, which preferentially targeted teachers over students (63% versus 22%). We observed lower crude secondary attack rates in schools with a ventilation system compared to schools without a ventilation system (1.2% versus 2.8%, p<0.01). Mandatory masking for staff members had no effect on the overall crude secondary attack rate (2.0% versus 2.1%, p = 0.03) but decreased the crude secondary attack rate among staff members (2.3% versus 1.7%, p<0.01). Schools varied in their implementation of NPIs, most of which targeted students. Rates of within-school transmission were higher compared to other studies, possibly due to a lack of proper ventilation. Our research may help improve guidance for primary schools in future outbreaks. [ABSTRACT FROM AUTHOR]

During the SARS-CoV-2 pandemic, many countries established wastewater (WW) surveillance to objectively monitor the level of infection within the population. As new variants continue to emerge, it has become clear that WW surveillance is an essential tool for the early detection of variants. The EU Commission published a recommendation suggesting an approach to establish surveillance of SARS-CoV-2 and its variants in WW, besides specifying the methodology for WW concentration and RNA extraction. Therefore, different groups have approached the issue with different strategies, mainly focusing on WW concentration methods, but only a few groups highlighted the importance of prefiltering WW samples and/or purification of RNA samples. Aiming to obtain high-quality sequencing data allowing variants detection, we compared four experimental conditions generated from the treatment of: i) WW samples by WW filtration and ii) the extracted RNA by DNase treatment, purification and concentration of the extracted RNA. To evaluate the best condition, the results were assessed by focusing on several sequencing parameters, as the outcome of SARS-CoV-2 sequencing from WW is crucial for variant detection. Overall, the best sequencing result was obtained by filtering the WW sample. Moreover, the present study provides an overview of some sequencing parameters to consider when optimizing a method for monitoring SARS-CoV-2 variants from WW samples, which can also be applied to any sample preparation methodology. [ABSTRACT FROM AUTHOR]

Objective: To describe the implementation of a test-negative design case-control study in California during the Coronavirus Disease 2019 (COVID-19) pandemic. Study design: Test-negative case-control study Methods: Between February 24, 2021 ‐ February 24, 2022, a team of 34 interviewers called 38,470 Californians, enrolling 1,885 that tested positive for SARS-CoV-2 (cases) and 1,871 testing negative for SARS-CoV-2 (controls) for 20-minute telephone survey. We estimated adjusted odds ratios for answering the phone and consenting to participate using mixed effects logistic regression. We used a web-based anonymous survey to compile interviewer experiences. Results: Cases had 1.29-fold (95% CI: 1.24–1.35) higher adjusted odds of answering the phone and 1.69-fold (1.56–1.83) higher adjusted odds of consenting to participate compared to controls. Calls placed from 4pm to 6pm had the highest adjusted odds of being answered. Some interviewers experienced mental wellness challenges interacting with participants with physical (e.g., food, shelter, etc.) and emotional (e.g., grief counseling) needs, and enduring verbal harassment from individuals called. Conclusions: Calls placed during afternoon hours may optimize response rate when enrolling controls to a case-control study during a public health emergency response. Proactive check-ins and continual collection of interviewer experience(s) and may help maintain mental wellbeing of investigation workforce. Remaining adaptive to the dynamic needs of the investigation team is critical to a successful study, especially in emergent public health crises, like that represented by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

A dynamic of virus adaptation and a mass vaccination campaign could significantly reduce the severity of clinical manifestations of COVID-19 and transmission. Hence, COVID-19 may become an endemic disease globally. Moreover, mass infection as the COVID-19 pandemic progressed affected the serology of the patients as a result of virus mutation and vaccination. Therefore, a need exists to acquire accurate serological testing to monitor the emergence of new outbreaks of COVID-19 to promptly prevent and control the disease spreading. In this study, the anti-Orf8 antibodies among samples collected in Thailand's first, fourth, and fifth waves of COVID-19 outbreaks compared with pre-epidemic sera were determined by indirect ELISA. The diagnostic sensitivity and specificity of the anti-Orf8 IgG ELISA for COVID-19 samples from the first, fourth, and fifth waves of outbreaks was found to be 100% compared with pre-epidemic sera. However, the diagnostic sensitivity and specificity of the anti-Orf8 IgG ELISA for a larger number of patient samples and controls from the fifth wave of outbreaks which were collected on day 7 and 14 after an RT-PCR positive result were 58.79 and 58.44% and 89.19 and 58.44%, respectively. Our data indicated that some of the controls might have antibodies from natural past infections. Our study highlighted the potential utility of anti-Orf8 IgG antibody testing for seroprevalence surveys but still warrants further investigations. [ABSTRACT FROM AUTHOR]

Background: Community-based mask wearing has been shown to reduce the transmission of SARS-CoV-2. However, few studies have conducted an economic evaluation of mask mandates, specifically in public transportation settings. This study evaluated the cost-effectiveness of implementing mask mandates for subway passengers in the United States by evaluating its potential to reduce COVID-19 transmission during subway travel. Materials and methods: We assessed the health impacts and costs of subway mask mandates compared to mask recommendations based on the number of infections that would occur during subway travel in the U.S. Using a combined box and Wells-Riley infection model, we estimated monthly infections, hospitalizations, and deaths averted under a mask mandate scenario as compared to a mask recommendation scenario. The analysis included costs of implementing mask mandates and COVID-19 treatment from a limited societal perspective. The cost-effectiveness (net cost per averted death) of mandates was estimated for three different periods based on dominant SARS-CoV-2 variants: Alpha, Beta, and Gamma (November 2020 to February 2021); Delta (July to October 2021); and early Omicron (January to March 2022). Results: Compared with mask recommendations only, mask mandates were cost-effective across all periods, with costs per averted death less than a threshold of $11.4 million (ranging from cost-saving to $3 million per averted death). Additionally, mask mandates were more cost-effective during the early Omicron period than the other two periods and were cost saving in January 2022. Our findings showed that mandates remained cost-effective when accounting for uncertainties in input parameters (e.g., even if mandates only resulted in small increases in mask usage by subway ridership). Conclusions: The findings highlight the economic value of mask mandates on subways, particularly during high virus transmissibility periods, during the COVID-19 pandemic. This study may inform stakeholders on mask mandate decisions during future outbreaks of novel viral respiratory diseases. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic was characterised by rapid waves of disease, carried by the emergence of new and more infectious SARS-CoV-2 virus variants. How the pandemic unfolded in various locations during its first two years has yet to be sufficiently covered. To this end, here we are looking at the circulating SARS-CoV-2 variants, their diversity, and hospitalisation rates in Estonia in the period from March 2000 to March 2022. Methods: We sequenced a total of 27,550 SARS-CoV-2 samples in Estonia between March 2020 and March 2022. High-quality sequences were genotyped and assigned to Nextstrain clades and Pango lineages. We used regression analysis to determine the dynamics of lineage diversity and the probability of clade-specific hospitalisation stratified by age and sex. Results: We successfully sequenced a total of 25,375 SARS-CoV-2 genomes (or 92%), identifying 19 Nextstrain clades and 199 Pango lineages. In 2020 the most prevalent clades were 20B and 20A. The various subsequent waves of infection were driven by 20I (Alpha), 21J (Delta) and Omicron clades 21K and 21L. Lineage diversity via the Shannon index was at its highest during the Delta wave. About 3% of sequenced SARS-CoV-2 samples came from hospitalised individuals. Hospitalisation increased markedly with age in the over-forties, and was negligible in the under-forties. Vaccination decreased the odds of hospitalisation in over-forties. The effect of vaccination on hospitalisation rates was strongly dependent upon age but was clade-independent. People who were infected with Omicron clades had a lower hospitalisation likelihood in age groups of forty and over than was the case with pre-Omicron clades regardless of vaccination status. Conclusions: COVID-19 disease waves in Estonia were driven by the Alpha, Delta, and Omicron clades. Omicron clades were associated with a substantially lower hospitalisation probability than pre-Omicron clades. The protective effect of vaccination in reducing hospitalisation likelihood was independent of the involved clade. [ABSTRACT FROM AUTHOR]

Introduction: Cases of the novel coronavirus disease (COVID-19) continue to spread around the world even one year after the declaration of a global pandemic. Those with weakened immune systems, due to immunosuppressive medications or disease, may be at higher risk of COVID-19. This includes individuals with autoimmune diseases, cancer, transplants, and dialysis patients. Assessing the risk and outcomes of COVID-19 in this population has been challenging. While administrative databases provide data with minimal selection and recall bias, clinical and behavioral data is lacking. To address this, we are collecting self-reported survey data from a randomly selected subsample with and without COVID-19, which will be linked to administrative health data, to better quantify the risk of COVID-19 infection associated with immunosuppression. Methods and analysis: Using administrative and laboratory data from British Columbia (BC), Canada, we established a population-based case-control study of all individuals who tested positive for SARS-CoV-2. Each case was matched to 40 randomly selected individuals from two control groups: individuals who tested negative for SARS-CoV-2 (i.e., negative controls) and untested individuals from the general population (i.e., untested controls). We will contact 1000 individuals from each group to complete a survey co-designed with patient partners. A conditional logistic regression model will adjust for potential confounders and effect modifiers. We will examine the odds of COVID-19 infection according to immunosuppressive medication or disease type. To adjust for relevant confounders and effect modifiers not available in administrative data, the survey will include questions on behavioural variables that influence probability of being tested, acquiring COVID-19, and experiencing severe outcomes. Ethics and dissemination: This study has received approval from the University of British Columbia Clinical Research Ethics Board [H20-01914]. Findings will be disseminated through scientific conferences, open access peer-reviewed journals, COVID-19 research repositories and dissemination channels used by our patient partners. [ABSTRACT FROM AUTHOR]

Background and aims: The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion. Methods: We searched Baidu News using the keywords "Novel Coronavirus" and "COVID-19" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data. Results: We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event. Conclusions: The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability. [ABSTRACT FROM AUTHOR]

Background: Conducting a study in rural pre-dominant areas will help to understand the penetration of the vaccination campaign during the COVID-19 health crisis. This study aimed to investigate vaccination coverage against COVID-19 among the rural adult population in India and to identify factors associated with vaccination coverage. Methods: A population-based cross-sectional study was conducted among the rural population in one district of north India from January to February 2023. A semi-structured questionnaire was designed on the SurveyMonkey digital platform for interviewing the participants, which consisted of questions related to socio-demographic profile, health problems, vaccination status, types of vaccine, re-infection after vaccination, and functional difficulties. The data regarding infection with COVID-19 was collected based on self-reported positive testing for SARS-CoV 2 on RT-PCR. Findings: A total of 3700 eligible individuals were enumerated for the survey, out of which 2954 (79.8%) were interviewed. The infection rate of past COVID-19 infection, based on self-report of testing positive, was 6.2% (95%CI: 5.3–7.1). Covishield vaccine was received by most participants (81.3%, 2380) followed by Covaxin (12.3%, 361) and Pfizer manufactured vaccine (0.03,1). The coverage for first, second, and booster doses of the vaccine was 98.2% (2902), 94.8% (2802), and 10.7% (315) respectively. The risk of reinfection at 12 months or more among participants with two doses of vaccine was 1.6% (46/2802, 95%CI: 1.2–2.1). The coverage among those with severe functional difficulties was lesser as compared to those with some or no difficulties. Interpretation: Vaccination coverage against COVID-19 in rural Haryana, India is not dependent on factors like gender or occupation but is dependent on age and education. Although the full and partial vaccination coverage is high, the booster dose coverage is poor. In addition, the presence of severe disability was significantly associated with reduced vaccination coverage. [ABSTRACT FROM AUTHOR]

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches. [ABSTRACT FROM AUTHOR]

The novel coronavirus, COVID-19, first identified in Wuhan, China, in December 2019, rapidly spread across the globe and resulted in significant public concern. In response, numerous countries have implemented guidelines aimed at promoting social distancing, limiting movement and gatherings, instituting lockdowns and curfews, and closing institutions. As a result, the media, including news releases, has become a primary source of information for many individuals (Laor & Lissitsa, 2022). The present study seeks to investigate differences between major television programs featured on public and commercial channels in Israel, utilizing the lens of framing theory. Specifically, twelve monologues broadcast simultaneously on both channels were analyzed to compare differences in content, topics, format, and accompanying visuals. The study's results indicate that both commercial and public channels exhibited similar behavior in shaping public opinion during the crisis period. Specifically, both channels displayed criticism of the government's conduct and utilized negative framing techniques while neglecting to provide space for popular voices. Nevertheless, certain distinctions between the commercial and public broadcasting spheres still persist, particularly in the realm of visual presentation, as some long-standing elements that typify each channel have endured over time. [ABSTRACT FROM AUTHOR]

Purpose: Since identified in December 2019, the novel coronavirus disease 2019 (COVID-19) has had a global impact on medical resource use and costs for patients with cancer in South Korea. This study aimed to identify the medical use and costs among patients with cancer during the COVID-19 pandemic, to predict these patterns in South Korea in the future. Methods: We conducted a secondary claims data analysis using the National Health Insurance Service database for the calendar period of 2019–2020. Monthly relative percent changes in cancer incidence, medical use, and billing costs for medical care utilization by cancer type were calculated. Then, the medical use and costs after January 2020 were predicted using a time series model with data before the COVID-19 outbreak (2014–2019). Results: The incidence of cancer diagnoses has seen a notable decline since the outbreak of the COVID-19 in 2020 as compared to 2019. Despite the impact of COVID-19, there hasn't been a distinct decline in outpatient utilization when compared to inpatient utilization. While medical expenses for both inpatient and outpatient visits have slightly increased, the number of patients treated for cancer has decreased significantly compared to the previous year. In June 2020, overall outpatient costs experienced the highest increase (21.1%), while individual costs showed the most significant decrease (-4.9%) in June 2020. Finally, the number of hospitalisations and outpatient visits increased slightly from June–July in 2020, reducing the difference between the actual and predicted values. The decrease in the number of inpatient hospitalisations (-22~-6%) in 2020 was also high. Conclusions: The overall use of medical services by patients with cancer decreased in 2020 compared with that in the pre-COVID-19 pandemic period. In the future, the government should consider how to recover from the COVID-19 pandemic, and establish permanent health policies for patients with cancer. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic has significantly affected blood donors worldwide. It is important for the blood service to return to its pre-pandemic level as soon as possible and to perform its functions fully. This study compared the donation and demographic profiles of blood and its component donors one year before and during three pandemic years in Lithuania. All blood and blood component donations (n = 413,358) and demographic characteristics of all donors from April 1, 2019, to March 31, 2023, were analyzed. All data were obtained from annual publications, and statistics were obtained from the Blood Donor Register. Data were analyzed using descriptive statistics. Following a 9.41 percent decrease in the first year of the pandemic, the quantity of blood and blood component donations increased by 3.49 percent in the third year compared to the pre-pandemic year. Throughout the three years of the pandemic, a statistically significant decrease in the proportion of first-time blood and blood component donations was observed. Both the number and proportion of donations by donors under 25 years old decreased during the pandemic. The proportion of pre-donation deferrals for all attempts to donate significantly decreased during the pandemic. There was a statistically significant lower prevalence of all positive transfusion-transmitted infectious (TTI) markers among all donations compared to the pre-pandemic year for all three pandemic years. The odds for hepatitis B virus, hepatitis C virus, and all TTI markers during the second and third pandemic years were significantly lower than those in the pre-pandemic year. In conclusion, most dimensions of blood and its component donations and donor characteristics have returned to pre-pandemic levels or show positive trends. However, the major concern is the remaining decrease in donations from first-time and donors under 25 years old. [ABSTRACT FROM AUTHOR]

Several studies have revealed that SARS-CoV-2 damages brain function and produces significant neurological disability. The SARS-CoV-2 coronavirus, which causes COVID-19, may infect the heart, kidneys, and brain. Recent research suggests that monoamine oxidase B (MAO-B) may be involved in metabolomics variations in delirium-prone individuals and severe SARS-CoV-2 infection. In light of this situation, we have employed a variety of computational to develop suitable QSAR model using PyDescriptor and genetic algorithm-multilinear regression (GA-MLR) models (R2 = 0.800–793, Q2LOO = 0.734–0.727, and so on) on the data set of 106 molecules whose anti-SARS-CoV-2 activity was empirically determined. QSAR models generated follow OECD standards and are predictive. QSAR model descriptors were also observed in x-ray-resolved structures. After developing a QSAR model, we did a QSAR-based virtual screening on an in-house database of 200 compounds and found a potential hit molecule. The new hit's docking score (-8.208 kcal/mol) and PIC50 (7.85 M) demonstrated a significant affinity for SARS-CoV-2's main protease. Based on post-covid neurodegenerative episodes in Alzheimer's and Parkinson's-like disorders and MAO-B's role in neurodegeneration, the initially disclosed hit for the SARS-CoV-2 main protease was repurposed against the MAO-B receptor using receptor-based molecular docking, which yielded a docking score of -12.0 kcal/mol. This shows that the compound that inhibits SARS-CoV-2's primary protease may bind allosterically to the MAO-B receptor. We then did molecular dynamic simulations and MMGBSA tests to confirm molecular docking analyses and quantify binding free energy. The drug-receptor complex was stable during the 150-ns MD simulation. The first computational effort to show in-silico inhibition of SARS-CoV-2 Mpro and allosteric interaction of novel inhibitors with MAO-B in post-covid neurodegenerative symptoms and other disorders. The current study seeks a novel compound that inhibits SAR's COV-2 Mpro and perhaps binds MAO-B allosterically. Thus, this study will enable scientists design a new SARS-CoV-2 Mpro that inhibits the MAO-B receptor to treat post-covid neurological illness. [ABSTRACT FROM AUTHOR]

Background: We sought to evaluate the long-term effects of COVID-19 on renal function in patients with biopsy-proven kidney diseases. Methods: A total of 451 patients with biopsy-proven kidney disease and at least 12 months of follow-up subsequent to COVID-19 pandemic onset were included in the study. The primary study endpoint was a composite of a persistent decline of more than 30% in eGFR or ESRD. Results: 23.1% of patients had COVID-19 during a follow-up period of 2.5 y (0.8–2.6), while 17.6% of patients reached the composite endpoint. Those with COVID-19 were more likely to reach the composite endpoint [26.7% vs. 14.8%; OR, 2.1 (95%CI, 1.23–3.58), p = 0.006). There was a significant eGFR change in the first year of follow-up between the two study groups [-2.24 (95%CI,-4.86; 0.37) vs. +2.31 (95%CI, 0.78; 3.85) ml/min, p = 0.004], with an adjusted mean difference of -4.68 ml/min (95%CI,-7.7; -1.59)(p = 0.03). The trend for worse renal outcomes remained consistent in patients with IgAN, MN and FSGS, but not in those with LN. After multivariate adjustment, the independent predictors of the composite endpoint were baseline eGFR (HR, 0.94; 95%CI, 0.92–0.95), COVID-19 (HR, 1.91; 1.16–3.12) and male gender (HR, 1.64; 95%CI, 1.01–2.66). In multivariate linear regression analysis, COVID-19 independently determined a reduction of eGFR at 12 months by 4.62 ml/min/1.73m2 (β coefficient, -4.62; 95%CI, -7.74 to -1.5, p = 0.004). Conclusions: There is a significant impact of COVID-19 on long-term renal function in patients with biopsy-proven kidney diseases, leading to a greater decline of eGFR and a worse renal survival. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has progressed rapidly, with the emergence of new virus variants that pose challenges in treating infected individuals. In Mexico, four epidemic waves have been recorded with varying disease severity. To understand the heterogeneity in clinical presentation over time and the sensitivity and specificity of signs and symptoms in identifying COVID-19 cases, an analysis of the changes in the clinical presentation of the disease was conducted. Aim: To analyze the changes in the clinical presentation of COVID-19 among 3.38 million individuals tested for SARS-CoV-2 at the Mexican Social Security Institute (IMSS) from March 2020 to October 2021 and evaluate the predictivity of signs and symptoms in identifying COVID-19 cases. Methods: A retrospective analysis of clinical presentation patterns of COVID-19 among individuals treated at IMSS was performed, contrasting the signs and symptoms among SARS-CoV-2-positive individuals with those who tested negative for the virus but had respiratory infection symptoms. The sensitivity and specificity of each sign and symptom in identifying SARS-CoV-2 infection were estimated. Results: The set of signs and symptoms reported for COVID-19-suspected patients treated at IMSS were not highly specific for SARS-CoV-2 positivity. The signs and symptoms exhibited variability based on age and epidemic wave. The area under the receiver operating characteristic (ROC) curve was 0.62 when grouping the five main symptoms (headache, dyspnea, fever, arthralgia, and cough). Most of the individual symptoms had ROC values close to 0.5 (16 out of 22 between 0.48 and 0.52), indicating non-specificity. Conclusions: The results highlight the difficulty in making a clinical diagnosis of COVID-19 due to the lack of specificity of signs and symptoms. The variability of clinical presentation over time and among age groups highlights the need for further research to differentiate whether the changes are due to changes in the virus, who is becoming infected, or the population, particularly with respect to prior infection and vaccination status. [ABSTRACT FROM AUTHOR]

The transmission of bacteria and respiratory viruses through expelled saliva microdroplets and aerosols is a significant concern for healthcare workers, further highlighted during the SARS-CoV-2 pandemic. To address this issue, the development of nanomaterials with antimicrobial properties for use as nanolayers in respiratory protection equipment, such as facemasks or respirators, has emerged as a potential solution. In this study, a silver and copper nanolayer called SakCu® was deposited on one side of a spun-bond polypropylene fabric using the magnetron sputtering technique. The antibacterial and antiviral activity of the AgCu nanolayer was evaluated against droplets falling on the material and aerosols passing through it. The effectiveness of the nanolayer was assessed by measuring viral loads of the enveloped virus SARS-CoV-2 and viability assays using respiratory surrogate viruses, including PaMx54, PaMx60, PaMx61 (ssRNA, Leviviridae), and PhiX174 (ssDNA, Microviridae) as representatives of non-enveloped viruses. Colony forming unit (CFU) determination was employed to evaluate the survival of aerobic and anaerobic bacteria. The results demonstrated a nearly exponential reduction in SARS-CoV-2 viral load, achieving complete viral load reduction after 24 hours of contact incubation with the AgCu nanolayer. Viability assays with the surrogate viruses showed a significant reduction in viral replication between 2–4 hours after contact. The simulated viral filtration system demonstrated inhibition of viral replication ranging from 39% to 64%. The viability assays with PhiX174 exhibited a 2-log reduction in viral replication after 24 hours of contact and a 16.31% inhibition in viral filtration assays. Bacterial growth inhibition varied depending on the species, with reductions ranging from 70% to 92% for aerobic bacteria and over 90% for anaerobic strains. In conclusion, the AgCu nanolayer displayed high bactericidal and antiviral activity in contact and aerosol conditions. Therefore, it holds the potential for incorporation into personal protective equipment to effectively reduce and prevent the transmission of aerosol-borne pathogenic bacteria and respiratory viruses. [ABSTRACT FROM AUTHOR]

The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic. [ABSTRACT FROM AUTHOR]

Early in the new coronavirus disease (COVID-19) pandemic, scholars and journalists noted partisan differences in behaviors, attitudes, and beliefs. Based on location data from a large sample of smartphones, as well as 13,334 responses to a proprietary survey spanning 10 months from April 1, 2020 to February 15, 2021, we document that the partisan gap has persisted over time and that the lack of convergence occurs even among individuals who were at heightened risk of death. Our results point to the existence and persistence of the interaction of partisanship and information acquisition and highlight the need for mandates and targeted informational campaigns towards those with high health risks. [ABSTRACT FROM AUTHOR]

Purpose: Post-Covid-19 syndrome is defined as the persistence of symptoms beyond 3 months after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The most common symptoms include reduced exercise tolerance and capacity, fatigue, neurocognitive problems, muscle pain and dyspnea. The aim of our work was to investigate exercise capacity and markers of subjective wellbeing and their independent relation to post-COVID-19 syndrome. Patients and methods: We examined a total of 69 patients with post-COVID-19 syndrome (23 male/46 female; age 46±12 years; BMI 28.9±6.6 kg/m2) with fatigue and a score ≥22 in the Fatigue Assessment Scale (FAS). We assessed exercise capacity on a cycle ergometer, a 6-minute walk test, the extent of fatigue (FAS), markers of health-related quality of life (SF-36 questionnaire) and mental health (HADS). Results: On average the Fatigue Assessment Scale was 35.0±7.4 points. Compared with normative values the VO2max/kg was reduced by 8.6±5.8 ml/min/kg (27.7%), the 6MWT by 71±96 m (11.9%), the health-related quality of life physical component score by 15.0±9.0 points (29.9%) and the mental component score by 10.6±12.8 points (20.6%). Subdivided into mild fatigue (FAS score 22–34) and severe fatigue (FAS score ≥35), patients with severe fatigue showed a significant reduction of the 6-minute walk test by 64±165 m (p<0.01) and the health-related quality of life physical component score by 5.8±17.2 points (p = 0.01). In multiple regression analysis age (β = –0.24, p = 0.02), sex (β = 0.22, p = 0.03), mental (β = –0.51, p<0.01) and physical (β = –0.44, p<0.01) health-related quality of life and by trend the 6-minute walk test (β = –0.22, p = 0.07) were associated with the FAS. Conclusion: Patients with post-COVID-19 syndrome show reduced maximal and submaximal physical performance as well as limitations in quality of life, particularly pronounced in the physical components. These results are essentially influenced by the severity of fatigue and implicating the need for targeted treatments. [ABSTRACT FROM AUTHOR]

In a previous study in Belgian nursing homes (NH) during the first wave of the COVID-19 pandemic, we found a SARS-CoV-2 seroprevalence of 17% with a large variability (0–45%) between NH. The current exploratory study aimed to identify nursing home-specific risk factors for high SARS-CoV-2 seroprevalence. Between October 19th, 2020 and November 13th, 2020, during the second COVID-19 wave in Belgium, capillary blood was collected on dried blood spots from 60 residents and staff in each of the 20 participating NH in Flanders and Brussels. The presence of SARS-CoV-2-specific IgG antibodies was assessed by ELISA. Risk factors were evaluated using a questionnaire, filled in by the director or manager of the NH. Assessed risk factors comprised community-related factors, resident-related factors, management and performance features as well as building-related aspects. The relation between risk factors and seroprevalence was assessed by applying random forest modelling, generalized linear models and Bayesian linear regression. The present analyses showed that the prevalence of residents with dementia, the scarcity of personal protective equipment (surgical masks, FFP2 masks, glasses and face shields), and inadequate PCR test capacity were related to a higher seroprevalence. Generally, our study put forward that the various aspects of infection prevention in NH require more attention and investment. This exploratory study suggests that the ratio of residents with dementia, the availability of test capacity and personal protective equipment may have played a role in the SARS-CoV-2 seroprevalence of NH, after the first wave. It underscores the importance of the availability of PPE and education in infection prevention. Moreover, investments may also yield benefits in the prevention of other respiratory infections (such as influenza). [ABSTRACT FROM AUTHOR]

This study utilizes the hedging potential of the U.S. Dollar Index (USDX) during the COVID-19 period, specifically comparing its positive effects on optimal portfolio weights and hedging ratios with those of traditional hedging assets, such as the VIX and gold. The scalar BEKK GARCH model is employed to forecast volatility and calculate hedging indicators. The results show that USDX exhibits strong hedging abilities against S&P 500 index volatility. These findings highlight the advantageous role of the USDX as a hedging instrument, particularly during periods of heightened market uncertainty, such as during the COVID-19 crisis. Despite the increased market volatility during the COVID-19 pandemic, the value of the optimal portfolio weights is stable and the volatility of the weights is significantly reduced, demonstrating the strength of the USDX's low risk and volatility in hedging against market fluctuations. Moreover, the increase in the hedge ratio indicates that more capital is allocated to hedging, reflecting the increased correlation between the USDX and S&P 500 index. These results emphasize the beneficial role of the USDX as a hedging instrument during times of elevated market uncertainty, such as during the COVID-19 crisis. Ultimately, USDX can provide valuable insights for market participants seeking effective hedging strategies. [ABSTRACT FROM AUTHOR]

Background: COVID-19 experiences on noncommunicable diseases (NCDs) from district-level hospital settings during waves I and II are scarcely documented. The aim of this study is to investigate the NCDs associated with COVID-19 severity and mortality in a district-level hospital with a high HIV/TB burden. Methods: This was a retrospective observational study that compared COVID-19 waves I and II at Khayelitsha District Hospital in Cape Town, South Africa. COVID-19 adult patients with a confirmed SARS-CoV-2 polymerase chain reaction (PCR) or positive antigen test were included. In order to compare the inter wave period, clinical and laboratory parameters on hospital admission of noncommunicable diseases, the Student t-test or Mann-Whitney U for continuous data and the X2 test or Fishers' Exact test for categorical data were used. The role of the NCD subpopulation on COVID-19 mortality was determined using latent class analysis (LCA). Findings: Among 560 patients admitted with COVID-19, patients admitted during wave II were significantly older than those admitted during wave I. The most prevalent comorbidity patterns were hypertension (87%), diabetes mellitus (65%), HIV/AIDS (30%), obesity (19%), Chronic Kidney Disease (CKD) (13%), Congestive Cardiac Failure (CCF) (8.8%), Chronic Obstructive Pulmonary Disease (COPD) (3%), cerebrovascular accidents (CVA)/stroke (3%), with similar prevalence in both waves except HIV status [(23% vs 34% waves II and I, respectively), p = 0.022], obesity [(52% vs 2.5%, waves II and I, respectively), p <0.001], previous stroke [(1% vs 4.1%, waves II and I, respectively), p = 0.046]. In terms of clinical and laboratory findings, our study found that wave I patients had higher haemoglobin and HIV viral loads. Wave II, on the other hand, had statistically significant higher chest radiography abnormalities, fraction of inspired oxygen (FiO2), and uraemia. The adjusted odds ratio for death vs discharge between waves I and II was similar (0.94, 95%CI: 0.84–1.05). Wave I had a longer average survival time (8.0 vs 6.1 days) and a shorter average length of stay among patients discharged alive (9.2 vs 10.7 days). LCA revealed that the cardiovascular phenotype had the highest mortality, followed by diabetes and CKD phenotypes. Only Diabetes and hypertension phenotypes had the lowest mortality. Conclusion: Even though clinical and laboratory characteristics differed significantly between the two waves, mortality remained constant. According to LCA, the cardiovascular, diabetes, and CKD phenotypes had the highest death probability. [ABSTRACT FROM AUTHOR]

Introduction: Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control's (CDC) discharge diagnosis-based algorithm and the Massachusetts state department's drug administration-based classification system on identifying admissions for COVID-19. Methods: In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity. Results: Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6–54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths. Conclusion: Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions. Trial registration: Clinicaltrials.gov, NCT04702945. [ABSTRACT FROM AUTHOR]

The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use. [ABSTRACT FROM AUTHOR]

While the World was busy mitigating the disastrous health and economic effects of the novel coronavirus, a less direct, but not less concerning peril has largely remained unexplored: the COVID-19 crisis may have disrupted some of the most fundamental social and political relationships in democratic societies. We interviewed samples resembling the national population of Denmark, Hungary, Italy and the US three times: in April, June and December of 2020 (14K observations). We show that multiple (but not all) measures of support for the political system decreased between April and December. Exploiting the panel setup, we demonstrate that within-respondent increases in indicators of pandemic fatigue (specifically, the perceived subjective burden of the pandemic and feelings of anomie) correspond to decreases in system support and increases in extreme anti-systemic attitudes. At the same time, we find no systematic trends in feelings of social solidarity, which are largely unaffected by changes in pandemic burden. [ABSTRACT FROM AUTHOR]

In Finland, the first wave of the COVID-19 epidemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) took place from March to June 2020, with the majority of COVID-19 cases diagnosed in the Helsinki-Uusimaa region. The magnitude and trend in the incidence of COVID-19 is one way to monitor the course of the epidemic. The diagnosed COVID-19 cases are a subset of the infections and therefore the COVID-19 incidence underestimates the SARS-CoV-2 incidence. The likelihood that an individual with SARS-CoV-2 infection is diagnosed with COVID-19 depends on the clinical manifestation as well as the infection testing policy and capacity. These factors may fluctuate over time and the underreporting of infections changes accordingly. Quantifying the extent of underreporting allows the assessment of the true incidence of infection. To obtain information on the incidence of SARS-CoV-2 infection in Finland, a series of serological surveys was initiated in April 2020. We develop a Bayesian inference approach and apply it to data from the serological surveys, registered COVID-19 cases, and external data on antibody development, to estimate the time-dependent underreporting of SARS-Cov-2 infections during the first wave of the COVID-19 epidemic in Finland. During the entire first wave, there were 1 to 5 (95% probability) SARS-CoV-2 infections for every COVID-19 case. The underreporting was highest before April when there were 4 to 17 (95% probability) infections for every COVID-19 case. It is likely that between 0.5%–1.0% (50% probability) and no more than 1.5% (95% probability) of the adult population in the Helsinki-Uusimaa region were infected with SARS-CoV-2 by the beginning of July 2020. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus disease has emerged as the most pressing global health issue. In women with COVID-19 disease, pregnancy confers a substantial additional risk of morbidity and mortality. Objective: This study aimed to assess WHO-recommended COVID-19 prevention practices and determinant factors among pregnant women attending antenatal care during the third wave of COVID-19 in eastern Ethiopia. Methods: An Institutional-based cross-sectional study was conducted among 422 pregnant women attending antenatal care in Harar, from October 10 to November 10, 2021. The sample size was proportionally allocated to all healthcare facilities, then the study participants were selected using systematic random sampling. Descriptive summary statistics were done. Logistic regression analyses were computed to identify associations between dependent and independent variables. Variables with a p-value < 0.05 were declared statistically significant. Result: Out of 422 pregnant women, 61.6% of them had good WHO Recommended COVID-19 Prevention Practices. Those with age 25–34 years (AOR: 9.7, 95%CI: 4.8, 19.3), age 35–44 years (AOR:4.8, 95%CI: 2.6, 9.03), monthly income > 10,000 ETB (AOR: 9.4, 95%CI: 2.1, 42.1), being a student (AOR: 10, 95%CI: 2.3, 47.1), having a good level of knowledge (AOR: 2.3, 95%CI:1.4, 3.8), and having ≥10 family members (AOR: 0.24, 95%CI: 0.06, 0.9) were found to have a significant association with WHO recommended prevention practice among pregnant women. Conclusion: Overall, the WHO-recommended COVID-19 prevention practice among pregnant women attending antenatal care was good, but it needs improvement. In order to improve prevention practices among pregnant women, Harari Regional Health Bureau and other stakeholders should provide repeated, targeted, and tailored information to pregnant women and the community at large through different media. [ABSTRACT FROM AUTHOR]