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The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students' performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students' performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features. [ABSTRACT FROM AUTHOR]

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

Most of the countries in the world are affected by the coronavirus epidemic that put people in danger, with many infected cases and deaths. The crowding factor plays a significant role in the transmission of coronavirus disease. On the other hand, the vaccines of the covid-19 played a decisive role in the control of coronavirus infection. In this paper, a fractional order epidemic model (SIVR) of coronavirus disease is proposed by considering the effects of crowding and vaccination because the transmission of this infection is highly influenced by these two factors. The nonlinear incidence rate with the inclusion of these effects is a better approach to understand and analyse the dynamics of the model. The positivity and boundedness of the fractional order model is ensured by applying some standard results of Mittag Leffler function and Laplace transformation. The equilibrium points are described analytically. The existence and uniqueness of the non-integer order model is also confirmed by using results of the fixed-point theory. Stability analysis is carried out for the system at both the steady states by using Jacobian matrix theory, Routh–Hurwitz criterion and Volterra-type Lyapunov functions. Basic reproductive number is calculated by using next generation matrix. It is verified that disease-free equilibrium is locally asymptotically stable if R 0 < 1 and endemic equilibrium is locally asymptotically stable if R 0 > 1 . Moreover, the disease-free equilibrium is globally asymptotically stable if R 0 < 1 and endemic equilibrium is globally asymptotically stable if R 0 > 1 . The non-standard finite difference (NSFD) scheme is developed to approximate the solutions of the system. The simulated graphs are presented to show the key features of the NSFD approach. It is proved that non-standard finite difference approach preserves the positivity and boundedness properties of model. The simulated graphs show that the implementation of control strategies reduced the infected population and increase the recovered population. The impact of fractional order parameter α is described by the graphical templates. The future trends of the virus transmission are predicted under some control measures. The current work will be a value addition in the literature. The article is closed by some useful concluding remarks. [ABSTRACT FROM AUTHOR]

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has imposed significant challenges on global health, emphasizing the persistent threat of large-scale infectious diseases in the future. This study addresses the need to enhance pooled testing efficiency for large populations. The common approach in pooled testing involves consolidating multiple test samples into a single tube to efficiently detect positivity at a lower cost. However, what is the optimal number of samples to be grouped together in order to minimize costs? i.e. allocating ten individuals per group may not be the most cost-effective strategy. In response, this paper introduces the hierarchical quotient space, an extension of fuzzy equivalence relations, as a method to optimize group allocations. In this study, we propose a cost-sensitive multi-granularity intelligent decision model to further minimize testing costs. This model considers both testing and collection costs, aiming to achieve the lowest total cost through optimal grouping at a single layer. Building upon this foundation, two multi-granularity models are proposed, exploring hierarchical group optimization. The experimental simulations were conducted using MATLAB R2022a on a desktop with Intel i5-10500 CPU and 8G RAM, considering scenarios with a fixed number of individuals and fixed positive probability. The main findings from our simulations demonstrate that the proposed models significantly enhance the efficiency and reduce the overall costs associated with pooled testing. For example, testing costs were reduced by nearly half when the optimal grouping strategy was applied, compared to the traditional method of grouping ten individuals. Additionally, the multi-granularity approach further optimized the hierarchical groupings, leading to substantial cost savings and improved testing efficiency. [ABSTRACT FROM AUTHOR]

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020–2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes"). [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]

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to analyzing the dynamics of COVID-19 using nonstandard finite difference (NSFD) schemes. Our model incorporates both asymptomatic and symptomatic infected individuals, allowing for a more comprehensive understanding of the epidemic's spread. We introduce an unconditionally stable NSFD system that eliminates the need for traditional Runge–Kutta methods, ensuring dynamical consistency and numerical accuracy. Through rigorous numerical analysis, we evaluate the performance of different NSFD strategies and validate our analytical findings. Our work demonstrates the benefits of using NSFD schemes for modeling infectious diseases, offering advantages in terms of stability and efficiency. We further illustrate the dynamic behavior of COVID-19 under various conditions using numerical simulations. The results from these simulations demonstrate the effectiveness of the proposed approach in capturing the epidemic's complex dynamics. [ABSTRACT FROM AUTHOR]

We conducted a large-scale online survey in February 2023 to investigate the public's perceptions of COVID-19 infection and fatality risks in Japan. We identified two key findings. First, univariate analysis comparing perceived and actual risk suggested overestimation and nonnegligible underestimation of COVID-19 risk. Second, multivariate logistic regression analyses revealed that age, income, education levels, health status, information sources, and experiences related to COVID-19 were associated with risk perceptions. Given that risk perceptions are closely correlated with daily socioeconomic activities and well-being, it is important for policy-makers and public health experts to understand how to communicate COVID-19 risk to the public effectively. [ABSTRACT FROM AUTHOR]

This study investigates the incidence of Class B respiratory infectious diseases (RIDs) in China under the Coronavirus disease 2019 (COVID-19) epidemic and examines variations post-epidemic, following the relaxation of non-pharmaceutical interventions (NPIs). Two-stage evaluation was used in our study. In the first stage evaluation, we established counterfactual models for the pre-COVID-19 period to estimate expected incidences of Class B RIDs without the onset of the epidemic. In the second stage evaluation, we constructed seasonal autoregressive integrated moving average intervention (SARIMA-Intervention) models to evaluate the impact on the Class B RIDs after NPIs aimed at COVID-19 pandemic were relaxed. The counterfactual model in the first stage evaluation suggested average annual increases of 10.015%, 78.019%, 70.439%, and 67.799% for tuberculosis, scarlet fever, measles, and pertussis respectively, had the epidemic not occurred. In the second stage evaluation, the total relative reduction in 2023 of tuberculosis, scarlet fever, measles and pertussis were − 35.209%, − 59.184%, − 4.481%, and − 9.943% respectively. The actual incidence declined significantly in the first stage evaluation. However, the results of the second stage evaluation indicated that a rebound occurred in four Class B RIDs after the relaxation of NPIs; all of these showed a negative total relative reduction rate. [ABSTRACT FROM AUTHOR]

Stochastic delayed modeling has a significant non-pharmaceutical intervention to control transmission dynamics of infectious diseases and its results are close to the reality of nature. The covid-19 has been controlled globally but there is still a threat and appears in different variants like omicron and SARS-CoV-2 etc. globally. This article, considered pattern a mathematical model based on Susceptible, Infected, and recovered populations with highly nonlinear incidence rates. we studied the dynamics of the coronavirus model; a newly proposed version is a stochastic delayed model that is based on nonlinear stochastic delayed differential equations (SDDEs). Transition probabilities and parametric perturbation methods were used for the construction of the stochastic delayed model. The fundamental properties like positivity, boundedness, existence and uniqueness, and stability results of equilibria of the model with certain conditions of reproduction number are studied regularly. Also, the extinction and persistence of disease are studied with the help of well-known theorems. The numerical methods used to find a visualization of results due to the complexity of stochastic delayed differential equations. Furthermore, for computational analysis, we implemented existing methods in the literature and compared their results with the proposed method like nonstandard finite difference for stochastic delayed model. The proposed method restores all dynamical properties of the model with a free choice of time steps. [ABSTRACT FROM AUTHOR]

Although COVID-19 infection is an immunosuppressant disease, many immunosuppressant agents, such as pulse methylprednisolone (PMP), dexamethasone (DXM), and tocilizumab (TCZ), were used during the pandemic. Secondary infections in patients with COVID-19 have been reported recently. This study investigated these agents' effects on secondary infections and outcomes in patients with COVID-19 in intensive care units (ICUs). This study was designed retrospectively, and all data were collected from the tertiary intensive care units of six hospitals between March 2020 and October 2021. All patients were divided into three groups: Group I [GI, PMP (−), DXM (−) and TCZ (−)], Group II [GII, PMP (+), DXM (+)], and Group III [GIII, PMP (+), DXM (+), TCZ (+)]. Demographic data, PaO/FiO2 ratio, laboratory parameters, culture results, and outcomes were recorded. To compare GI-GII and GI-GIII, propensity score matching (PSM) was used by matching 14 parameters. Four hundred twelve patients with COVID-19 in the ICU were included in the study. The number of patients with microorganisms ≥ 2 was 279 (67.7%). After PSM, in GII and GIII, the number of (+) tracheal cultures and (+) bloodstream cultures detected different microorganisms ≥ 2 during the ICU period, neuropathy, tracheotomized patients, duration of IMV, and length of ICU stay were significantly higher than GI. The mortality rate was similar in GI and GII, whereas it was significantly higher in GIII than in GI. The use of immunosuppressant agents in COVID-19 patients may lead to an increase in secondary infections. In addition, increased secondary infections may lead to prolonged ICU stay, prolonged IMV duration, and increased mortality. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic brought chemosensory impairment to the forefront of medicine, revealing gaps in the knowledge of pathophysiological mechanisms, true prevalence and preventive/therapeutic alternatives. This is a sub-study of the ORCHESTRA cohort focusing on post-COVID-19 chemosensory symptoms. Risk factors for neurosensorial cluster of post-COVID-19 syndrome (NSc-PCS) were assessed through multivariable analysis. Psychophysical validated tests were applied on a sub-population of 50 patients. Qualitative chemosensory symptoms as well as nasal and oral chemesthesis were evaluated through anamnestic interview and the quality of life through the SF-36 questionnaire. Chemosensory symptoms evolution and olfactory training's outcome were assessed through phone-call interviews. Out of 1187 patients (female, N = 630), 550 (47%) presented NSc-PCS, with a lower risk for older age and monoclonal antibodies treatment, and a higher risk in females (p < 0.001). Out of the 50 patients evaluated with psychophysical tests, 66% showed smell reduction with a qualitative alteration in 50% of hyposmic and 35% of normosmic patients. Hypogeusia was present in 14 (28%) of the patients assessed, with 56% showing a qualitative alteration; 53% of normogeusic patients presented qualitative disorders. NSc-PCS has a complex, fluctuating, multifaceted presentation. Quantifying and characterizing COVID-19-related chemosensory impairment is key to understand underlying mechanisms and to develop preventive and therapeutic treatment. [ABSTRACT FROM AUTHOR]

Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a catastrophic impact on public health, extending to the food system and people's livelihoods worldwide, including Bangladesh. This study aimed to ascertain the COVID-19 pandemic impacts on livelihood assets in the North-Western areas (Rajshahi and Rangpur) of Bangladesh. Primary data were collected from 320 farmers engaged in high-value agriculture using a multistage sampling method. The data were analysed using first-order structural equation modelling. The findings reveal a significant impact (p < 0.01) of the pandemic on all livelihood assets in Bangladesh. Notably, human assets exhibited the highest impact, with a coefficient of 0.740, followed sequentially by financial (0.709), social (0.684), natural (0.600), physical (0.542), and psychological (0.537) assets. Government-imposed lockdowns and mobility restrictions were identified as the major causes of the pandemic's negative effects on livelihoods, which included lost income, rising food prices, decreased purchasing power, inadequate access to food and medical supplies, increased social insecurity, and a rise in depression, worry, and anxiety among farmers. The effects of COVID-19 and associated policy measures on the livelihoods of high-value crop farmers have reversed substantial economic and nutritional advances gained over the previous decade. This study suggests attention to the sustainable livelihoods of farmers through direct cash transfer and input incentive programs to minimize their vulnerability to a pandemic like COVID-19 or any other crisis in the future. [ABSTRACT FROM AUTHOR]

Among malignant diseases, lung cancer has one of the highest mortality and incidence. Most epidemiological studies conclude that Hungary faces the most severe burden in association with this disease. However, for various reasons estimates and population-based studies show discrepancies. In this study, an intense data cleansing was performed on lung cancer cases that were reported to the Hungarian National Cancer Registry in 2018, and the major clinico-pathological parameters as well as survival characteristics were described. Our population-based figures were compared to the European estimates. As a result of our thorough revision, the corrected incidence of lung cancer has fallen below the number of cases that were reported to the Registry from 11,746 to 9,519. We also demonstrate that Hungary did not show the highest incidence and mortality in Europe, but it is still among the ones with the worst raking countries, with 92.9 and 50.6 age standardized rate per 100 thousand capita among males and females, respectively. Analysis of the annually reported case numbers revealed a gender-specific difference in incidence trends: while from 2001 to 2019 it slightly decreased among males, it increased among females. The most dominant subtype was adenocarcinoma, which was more frequent among female patients. Unfortunately, most of the newly diagnosed cases were in advanced stage; thus, 5 year overall survival was 14.8%. We anticipate that in the longer term, a decrease in incidence and improvement in survival rates may be expected as a result of the development of primary and secondary prevention programs in the country. [ABSTRACT FROM AUTHOR]

The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutics given the lack of specificity with symptom-based identification of LC and the overlap of symptoms with other chronic inflammatory conditions. Here, we report a machine-learning approach to LC/PASC diagnosis on 347 individuals using cytokine hubs that are also capable of differentiating LC from chronic lyme disease (CLD). We derived decision tree, random forest, and gradient-boosting machine (GBM) classifiers and compared their diagnostic capabilities on a dataset partitioned into training (178 individuals) and evaluation (45 individuals) sets. The GBM model generated 89% sensitivity and 96% specificity for LC with no evidence of overfitting. We tested the GBM on an additional random dataset (106 LC/PASC and 18 Lyme), resulting in high sensitivity (97%) and specificity (90%) for LC. We constructed a Lyme Index confirmatory algorithm to discriminate LC and CLD. [ABSTRACT FROM AUTHOR]

Formative research is an important component of health communication campaign development. Rapid message testing approaches are useful for testing new messaging quickly and efficiently during public health emergencies, such as COVID-19, when guidance and recommendations are rapidly changing. Wiki surveys simultaneously collect quantitative message testing data and qualitative feedback on potential social media campaign messages. Philly CEAL used wiki surveys to test messages about COVID-19 vaccinations for dissemination on social media. A cross-sectional survey of Philadelphia residents (N = 199) was conducted between January and March 2023. Wiki surveys were used to assess the perceived effectiveness of messages promoting the updated COVID-19 booster and child vaccination. In each wiki survey, participants were presented with two messages and asked to select the one that they perceived as most effective. Participants could alternatively select "can't decide" or submit their own message. A score estimating the probability of selection was calculated for each message. Participant-generated messages were routinely reviewed and incorporated into the message pool. Participants cast a total of 32,281 votes on messages seeded by the research team (n = 20) and participants (n = 43). The highest scoring messages were those that were generated by participants and spoke to getting your child vaccinated to protect them against serious illness and getting the booster to protect your health and that of your community. These messages were incorporated into social media posts disseminated by Philly CEAL's social media accounts. Wiki surveys are a feasible and efficient method of rapid message testing for social media campaigns. [ABSTRACT FROM AUTHOR]

Post COVID-19 Condition (PCC) is a clinical syndrome following COVID-19 disease. PCC symptoms in adults entail significant productivity loss and reduced quality of life. This study aimed at estimating the epidemiological and economic burden of PCC among the working-age population of Italy and the US. This ecological analysis was conducted on data from January 2020 to April 2023, regarding population aged 18–64. PCC incidence for the US was retrieved from publicly reported estimates, while for Italy it was estimated from COVID-19 cases. Prevalence of factors associated with PCC and parameters to calculate temporary productivity losses (TPL) were retrieved. An estimated incidence rate ratio (eIRR) of PCC incidence in Italy and the US was calculated. TPL for reduced earnings and total quality-adjusted life years (QALYs) lost were also estimated. The ecological eIRR Italy/US was 0.842 [95%CI 0.672–1.015], suggesting that, holding COVID-19 cases constant, 15.8% fewer PCC cases have occurred in Italy compared to the US. Overall PCC cases were found to be 12.0 [95%CI 9.9–14.1] million in the US, with 1.9 [95%CI 1.6–2.3] million QALYs lost, and 2.4 [95%CI 1.8–3.0] million in Italy, with 0.4 [95%CI 0.3–0.5] million QALYs lost. Up to April 2023, the TPL was estimated to be Int$7.5 [95%CI 5.8–10.1] billion in Italy and $41.5 [95%CI 34.3–48.7] billion in the US. PCC has had a significant epidemiological and economic impact on the working-age population. The findings from this study may be of use for health planning and policy regarding PCC in working-age adults. [ABSTRACT FROM AUTHOR]

Vitamin D has shown antimicrobial effects. This study aimed to explore the antiviral effects of vitamin D3 on saliva samples collected from patients with coronavirus disease-19 (COVID-19) and compare saliva and swab results to aid in policy development. Saliva and swab samples were collected from adult patients with a positive test for COVID-19 at the King Faisal Specialist Hospital and Research Centre, Jeddah. Patients who were immunocompromised and pregnant and aged < 18 years were excluded. Vitamin D3 compound (100, 300, 800, and 1,200 IU) was added to the first saliva sample in the laboratory (n = 20); the rest of the swab specimens were compared with the saliva samples via real-time polymerase chain reaction. Of the 257 patients, 236 (94.8%) had positive saliva sample test results, 7 (2.8%) had errors, and 6 (2.4%) had negative results. Of the 236 positive tests, 235 (99.6%) had a cycle threshold (Ct) indicating strong positive reactions, and only one (Ct = 28.86) was weak. Among the 236 positive results, 235 (99.6%) exhibited robust positive reactions, indicating a substantial positive sample size. Thus, saliva might be a dependable alternative testing tool when obtaining swab samples from patients is inconvenient or challenging. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD). The goal of this study is to use classical machine learning approaches to classify tweets into COVID-PTSD positive or negative categories. To this end, we employed various Machine Learning (ML) classifiers, to segregate the psychotic difficulties with the user's PTSD in the context of COVID-19, including Random Forest Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor. ML models are trained and tested using various combinations of feature selection strategies to get the best possible combination. Based on our experimentation on real-world dataset, we demonstrate our model's effectiveness to perform classification with an accuracy of 83.29% using Support Vector Machine as classifier and unigram as a feature pattern. [ABSTRACT FROM AUTHOR]

The coronavirus (COVID-19) pandemic has caused financial hardship and psychological distress among young Australians. This study investigates whether the Australian Government's emergency cash transfer payments—specifically welfare expansion for those unemployed prior to the pandemic (known in Australia as the Coronavirus Supplement) and JobKeeper (cash support for those with reduced or stopped employment due to the pandemic)—were associated with individual's level of coping during the coronavirus pandemic among those with and without mental disorders (including anxiety, depression, ADHD and autism). The sample included 902 young adults who participated in all of the last three waves (8, 9C1, 9C2) of the Longitudinal Study of Australian Children (LSAC), a nationally representative cohort study. Modified Poisson regression models were used to assess the impact of emergency cash transfer payments on 18–22-year-old's self-rated coping level, stratifying the analysis by those with and without mental disorders. All models were adjusted for gender, employment, location, family cohesion, history of smoking, alcohol intake, and COVID-19 test result. Of the 902-person sample analysed, 41.5% (n = 374) reported high levels of coping, 18.9% (n = 171) reported mental disorders, 40.3% (n = 364) received the Coronavirus Supplement and 16.4% (n = 148) received JobKeeper payments. Analysing the total sample demonstrated that participants who received the JobKeeper payment were more likely to have a higher level of coping compared to those who did not receive the JobKeeper payment. Stratified analyses demonstrated that those with pre-existing mental disorder obtained significant benefit from the JobKeeper payment on their level of coping, compared to those who did not receive JobKeeper. In contrast, receipt of the Coronavirus Supplement was not significantly associated with higher level of coping. Among those with no mental health disorder, neither the Coronavirus Supplement nor JobKeeper had a statistically significant impact on level of coping. These findings suggest the positive impacts of cash transfers on level of coping during the pandemic were limited to those with a pre-existing mental disorder who received JobKeeper. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic deeply affected healthcare workers, although the impact may have differed according to different workplace contexts. The aim of this current research was to compare the psychopathology presented by hospital versus nursing home healthcare workers during the COVID-19 pandemic and to analyse the predictive role of purpose in life and moral courage in the appearance of psychopathology. This was an observational, cross-sectional study carried out on a sample of 108 healthcare workers, 54 each from a hospital or nursing homes, who were recruited during the 5 and 6th waves of the COVID-19 pandemic in Spain. Various self-reported scales were used to assess anxiety, depression, acute/post-traumatic stress disorder, drug and alcohol abuse, burnout, purpose in life, and moral courage. Compared to the hospital healthcare workers, nursing home healthcare workers had higher scores and a higher prevalence of anxiety (74.1% vs. 42%), depression (40.7% vs. 14.8%), and post-traumatic stress disorder (55.6% vs. 25.9). In the overall sample, purpose in life was a protective factor against psychopathology (OR = 0.54) and burnout (OR = 0.48); moral courage was a protective factor against depression (OR = 0.47) and acute stress (OR = 0.45); and exposure of family/friends to SARS-CoV-2 was a risk factor for acute stress (OR = 2.24), post-traumatic stress disorder (OR = 1.33), and higher burnout depersonalisation subscale scores (OR = 1.84). In conclusion, the increased presence of psychopathology in nursing home healthcare workers may be influenced by workplace and occupational contexts, personal factors such as exposure of family/friends to SARS-CoV-2, or internal dimensions such as purpose in life and moral courage. This knowledge could be useful for understanding how a future epidemic or pandemic might affect the mental health of healthcare workers in different labour contexts. [ABSTRACT FROM AUTHOR]

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions. [ABSTRACT FROM AUTHOR]

Since March 2020, South Korea has implemented social distancing measures to reduce the spread of the coronavirus disease (COVID-19). These policies resulted in significant changes in the lifestyle of Korean adults, including spending more time indoors and experiencing increased sedentary time during the pandemic. This study aimed to investigate the combined influence of sedentary lifestyle and the COVID-19 pandemic on obesity rates among adults residing in South Korea. Data were obtained from the 2018–2020 Korea National Health and Nutrition Examination Survey (KNHANES). Multiple logistic regression was used to analyze the synergistic effects of a sedentary lifestyle and COVID-19 on obesity. The prevalence of sedentary lifestyles among the participants increased from 30.4% pre-COVID-19 to 36.6% during the pandemic. Furthermore, the obesity rates were raised independently by COVID-19 (OR: 1.16, 95% CI 1.04–1.30) and by a sedentary lifestyle (OR = 1.17, 95% CI 1.04–1.31). A synergistic effect of a sedentary lifestyle and COVID-19 on obesity was identified (OR: 1.57, 95% CI 1.30–1.85). COVID-19 and a sedentary lifestyle significantly increased the risk of obesity in South Korean adults. Moreover, the combined synergistic effects of these risk factors were too substantial to be disregarded by merely summing their individual effects on obesity. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly impacted the mental health of children and adolescents worldwide. While various factors contributing to this phenomenon have been explored, the role of learning style preferences remains underexamined. This cross-sectional study, conducted between October and November 2020, involved over 20,000 participants, including students from junior high school, senior high school, and undergraduate levels. Significant differences in depressive mood levels were observed among individuals with different learning modality preferences upon the return to traditional, in-person learning. Students with in-person learning preferences exhibited lower depression levels, while students with no preference for either learning style outperformed devotees of remote learning. The highest depression occurred among those endorsing neither learning mode. Importantly, the impact of these preferences on depressive moods was found to be moderated by COVID-19 infection experiences. The findings highlight the nuanced relationship between learning style preferences, COVID-19 infection experiences, and mental health outcomes among students. Acknowledging the moderating effect of infection experiences is crucial for developing targeted interventions and adapting pedagogical approaches post-pandemic. This study contributes valuable insights into the complex relationships shaping the mental well-being of students amidst educational disruptions caused by the pandemic. [ABSTRACT FROM AUTHOR]

High-dose inhaled Nitric Oxide (iNO) has been shown to have anti-inflammatory, vasodilator, and antimicrobial properties, resulting in improved arterial oxygenation as well as a beneficial therapeutic effect on lower respiratory tract infections. This study evaluated the safety and efficacy of 150-ppm intermittent iNO administered with a novel iNO-generator, for treating adults hospitalised for viral pneumonia. In this prospective, open-label, multicenter study, subjects aged 18–80, diagnosed with viral pneumonia received either standard supportive treatment alone (Control-Group) or combined with iNO for 40 min, 4 times per day up to 7 days (Treatment-Group). Out of 40 recruited subjects, 35 were included in the intention-to-treat population (34 with COVID-19). Adverse Events rate was similar between the groups (56.3% vs. 42.1%; respectively). No treatment-related adverse events were reported, while 2 serious adverse events were accounted for by underlying pre-existing conditions. Among the Treatment-Group, oxygen support duration was reduced by 2.7 days (Hazard Ratio = 2.8; p = 0.0339), a greater number of subjects reached oxygen saturation ≥ 93% within hospitalisation period (Hazard Ratio = 5.4; p = 0.049), and a trend for earlier discharge was demonstrated. Intermittent 150-ppm iNO-treatment is well-tolerated, safe, and beneficial compared to usual care for spontaneously breathing hospitalised adults diagnosed with COVID-19 viral pneumonia. [ABSTRACT FROM AUTHOR]

This study introduces a fractional order model to investigate the dynamics of polio disease spread, focusing on its significance, unique results, and conclusions. We emphasize the importance of understanding polio transmission dynamics and propose a novel approach using a fractional order model with an exponential decay kernel. Through rigorous analysis, including existence and stability assessment applying the Caputo Fabrizio fractional operator, we derive key insights into the disease dynamics. Our findings reveal distinct disease-free equilibrium (DFE) and endemic equilibrium (EE) points, shedding light on the disease's stability. Furthermore, graphical representations and numerical simulations demonstrate the behavior of the disease under various parameter values, enhancing our understanding of polio transmission dynamics. In conclusion, this study offers valuable insights into the spread of polio and contributes to the broader understanding of infectious disease dynamics. [ABSTRACT FROM AUTHOR]

COVID-19 disease is associated with a hyperinflammatory, pro-thrombotic state and a high mortality. Our primary objective was to assess the change in inflammatory and thrombotic markers associated with PEX, and secondary objectives were to assess the effects of PEX on progression of respiratory failure and incidence of acute thrombotic events. We conducted a prospective, phase II, non-blinded randomised control trial of plasma exchange compared to standard of care in critically ill adults with severe COVID-19 associated respiratory failure, requiring supplemental oxygen or ventilatory support and elevated thrombo-inflammatory markers (LDH, CRP, ferritin, and D-Dimer). Patients randomised to receive PEX were treated with a daily single volume plasma exchange for a minimum of five days. Twenty-two patients were randomised of who 11 received PEX. Demographic and clinical characteristics were similar between groups at presentation. PEX was associated with a significant reduction in pro-thrombotic markers FVIII, VWF and VWF Ag: ADAMTS 13 ratio (p < 0.001). There were no differences in the reduction of inflammatory markers, severity of respiratory failure (p = 0.7), thrombotic events (p = 0.67), or mortality (p > 0.99) at 28 days. PEX successfully reduced pro-thrombotic markers, although was not associated with reduction in inflammatory markers, respiratory failure, or thrombotic events. Trial registration: (NCT04623255); first posted on 10/11/2020. [ABSTRACT FROM AUTHOR]

Intensive care unit-acquired infections are complicating events in critically ill patients. In this study we analyzed the incidence, microbiological patterns, and outcome in patients with COVID-19 versus influenza in the intensive care unit (ICU). We included all adult patients treated with invasive mechanical ventilation due to (1) COVID-19 between January 2020 and March 2022, and (2) influenza between January 2015 and May 2023 at Sahlgrenska University Hospital, Sweden. Of the 480 participants included in the final analysis, 436 had COVID-19. The incidence rates of ICU-acquired infections were 31.6/1000 and 9.9/1000 ICU-days in the COVID-19 and influenza cohorts, respectively. Ventilator-associated lower respiratory tract infections were most common in both groups. In patients with COVID-19, corticosteroid treatment was associated with an increased risk of ICU-acquired infections and with higher 90-day mortality in case of infection. Furthermore, ICU-acquired infection was associated with a prolonged time in the ICU, with more difficult-to-treat gram-negative infections in late versus early ventilator-associated lower respiratory tract infections. Further research is needed to understand how the association between corticosteroid treatment and incidence and outcome of ICU-acquired infections varies across different patient categories. [ABSTRACT FROM AUTHOR]

COVID-19 is linked to diabetes, increasing the likelihood and severity of outcomes due to hyperglycemia, immune system impairment, vascular problems, and comorbidities like hypertension, obesity, and cardiovascular disease, which can lead to catastrophic outcomes. The study presents a novel COVID-19 management approach for diabetic patients using a fractal fractional operator and Mittag-Leffler kernel. It uses the Lipschitz criterion and linear growth to identify the solution singularity and analyzes the global derivative impact, confirming unique solutions and demonstrating the bounded nature of the proposed system. The study examines the impact of COVID-19 on individuals with diabetes, using global stability analysis and quantitative examination of equilibrium states. Sensitivity analysis is conducted using reproductive numbers to determine the disease's status in society and the impact of control strategies, highlighting the importance of understanding epidemic problems and their properties. This study uses two-step Lagrange polynomial to analyze the impact of the fractional operator on a proposed model. Numerical simulations using MATLAB validate the effects of COVID-19 on diabetic patients and allow predictions based on the established theoretical framework, supporting the theoretical findings. This study will help to observe and understand how COVID-19 affects people with diabetes. This will help with control plans in the future to lessen the effects of COVID-19. [ABSTRACT FROM AUTHOR]

This study aimed to assess the impact of the COVID-19 pandemic on general practitioner (GP), specialist, and dentist visits among 40 million Iranians covered by the Social Security Organization (SSO). A monthly interrupted time series analysis was conducted over a period of 72 months, including—47 months before the pandemic and 25 months after its onset. The outcomes variables were monthly number of GP, specialist, and dentist visits per 1000 SSO-insured individuals. The analysis was performed by total visits, visits to the SSO direct sector, and visits to the indirect sectors. The study found that in the first month of the pandemic, the number of visits per 1000 insured individuals significantly decreased for visits to GPs (by 51.12, 95% CI −64.42 to −37.88), visits to specialists (by 39.11, 95% CI −51.61 to −26.62), and visits to dentists (by 6.67, 95% CI −8.55 to −4.78). However, during the subsequent months of the pandemic, there was a significant increase in the number of monthly visits for all three categories, with GPs experiencing the highest increase (1.78 visits per 1000 insured), followed by specialists (1.32 visits per 1000 insured), and dentists (0.05 visits per 1000 insured). Furthermore, prior to the pandemic, the number of monthly GP visits per 1000 insured individuals was statistically significantly lower in the indirect sector compared to the direct sector (45.79, 95% CI −52.69 to −38.89). Conversely, the direct sector exhibited lower rates of specialist visits (25.84 visits per 1000 insured individuals, 95% CI 22.87 to 28.82) and dentist visits (0.75 visits per 1000 insured individuals, 95% CI 0.12 to 1.36) compared to the indirect sector. Additionally, the study found that in the first month of the pandemic, the monthly number of GP visits in the indirect sector significantly increased by 34.44 times (95% CI 24.81 to 44.08) compared to the direct sector. For specialist visits and dentist visits, the increase was 3.41 (95% CI −5.87 to 12.69) and 5.01 (95% CI 3.48 to 6.53) per 1000 insured individuals, respectively. Overall, the findings of this study demonstrate statistically significant disruptions in GP, specialist, and dentist visits during the COVID-19 pandemic, although some recovery was observed. Both the direct and indirect sectors experienced decreased visits. [ABSTRACT FROM AUTHOR]

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy is an inevitable risk for societies as it contributes to outbreaks of diseases. Prior research suggests that vaccination decisions of individuals tend to spread within social networks, resulting in a tendency to vaccination homophily. The clustering of individuals resistant to vaccination can substantially make the threshold necessary to achieve herd immunity harder to reach. In this study, we examined the extent of vaccination homophily among social contacts and its association with vaccine uptake during the COVID-19 pandemic in Hungary using a contact diary approach in two cross-sectional surveys. The results indicate strong clustering among both vaccinated and unvaccinated groups. The most powerful predictor of vaccine uptake was the perceived vaccination rate within the egos' social contact network. Vaccination homophily and the role of the interpersonal contact network in vaccine uptake were particularly pronounced in the networks of close relationships, including family, kinship, and strong social ties of the ego. Our findings have important implications for understanding COVID-19 spread dynamics by showing that the strong clustering of unvaccinated individuals posed a great risk in preventing the spread of the disease. [ABSTRACT FROM AUTHOR]

The widespread adoption of smartphones coupled with advancements in artificial intelligence has significantly propelled the use of intelligent personal assistants (IPAs). These digital assistants have become indispensable for many users, particularly during the COVID-19 pandemic. Employing coviance-based structural equation modeling (CB-SEM) and analyzing data from 260 participants, this study explores the key factors influencing IPA usage intensity. Contrary to expectations, affective risk perception showed no significant impact on either IPA usage or parasocial interaction during the pandemic. In stark contrast, cabin fever syndrome significantly influenced both IPA usage and parasocial interaction, underscoring the role of environmental and psychological stressors in shaping technology use. Furthermore, loneliness was found to significantly enhance parasocial interaction with IPAs, though it did not affect usage intensity. The findings highlight a substantial connection between parasocial interaction and IPA usage intensity, suggesting that users who engage in human-like interactions with IPAs tend to use them more extensively. These insights not only deepen our understanding of how IPAs are utilized during health crises but also point to potential directions for developing IPAs that are more responsive to users' emotional and social needs. [ABSTRACT FROM AUTHOR]

This study determined whether compared to conventional mechanical ventilation (MV), extracorporeal membrane oxygenation (ECMO) is associated with decreased hospital mortality or fibrotic changes in patients with COVID-19 acute respiratory distress syndrome. A cohort of 72 patients treated with ECMO and 390 with conventional MV were analyzed (February 2020–December 2021). A target trial was emulated comparing the treatment strategies of initiating ECMO vs no ECMO within 7 days of MV in patients with a PaO2/FiO2 < 80 or a PaCO2 ≥ 60 mmHg. A total of 222 patients met the eligibility criteria for the emulated trial, among whom 42 initiated ECMO. ECMO was associated with a lower risk of hospital mortality (hazard ratio [HR], 0.56; 95% confidence interval [CI] 0.36–0.96). The risk was lower in patients who were younger (age < 70 years), had less comorbidities (Charlson comorbidity index < 2), underwent prone positioning before ECMO, and had driving pressures ≥ 15 cmH2O at inclusion. Furthermore, ECMO was associated with a lower risk of fibrotic changes (HR, 0.30; 95% CI 0.11–0.70). However, the finding was limited due to relatively small number of patients and differences in observability between the ECMO and conventional MV groups. [ABSTRACT FROM AUTHOR]

Stay-at-home orders were introduced in many countries during the COVID-19 pandemic, limiting the time people spent outside their home and the attendance of gatherings. In this study, we argue from a theoretical model that in many cases the effect of such stay-at-home orders on incidence growth should be quadratic, and that this statement should also hold beyond COVID-19. That is, a reduction of the out-of-home duration to, say, 70% of its original value should reduce incidence growth and thus the effective R-value to 70 % · 70 % = 49 % of its original value. We then show that this hypothesis can be substantiated from data acquired during the COVID-19 pandemic by using a multiple regression model to fit a combination of the quadratic out-of-home duration and temperature to the COVID-19 growth multiplier. We finally demonstrate that many other models, when brought to the same scale, give similar reductions of the effective R-value, but that none of these models extend plausibly to an out-of-home duration of zero. [ABSTRACT FROM AUTHOR]

Accurate screening of COVID-19 infection status for symptomatic patients is a critical public health task. Although molecular and antigen tests now exist for COVID-19, in resource-limited settings, screening tests are often not available. Furthermore, during the early stages of the pandemic tests were not available in any capacity. We utilized an automated machine learning (ML) approach to train and evaluate thousands of models on a clinical dataset consisting of commonly available clinical and laboratory data, along with cytokine profiles for patients (n = 150). These models were then further tested for generalizability on an out-of-sample secondary dataset (n = 120). We were able to develop a ML model for rapid and reliable screening of patients as COVID-19 positive or negative using three approaches: commonly available clinical and laboratory data, a cytokine profile, and a combination of the common data and cytokine profile. Of the tens of thousands of models automatically tested for the three approaches, all three approaches demonstrated > 92% sensitivity and > 88 specificity while our highest performing model achieved 95.6% sensitivity and 98.1% specificity. These models represent a potential effective deployable solution for COVID-19 status classification for symptomatic patients in resource-limited settings and provide proof-of-concept for rapid development of screening tools for novel emerging infectious diseases. [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]

This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-ZICU: − 0.821 (p-value: 0.412); Spiegelhalter-Zdeath: 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection. [ABSTRACT FROM AUTHOR]

Wastewater based epidemiology has become a widely used tool for monitoring trends of concentrations of different pathogens, most notably and widespread of SARS-CoV-2. Therefore, in 2022, also in Rhineland–Palatinate, the Ministry of Science and Health has included 16 wastewater treatment sites in a surveillance program providing biweekly samples. However, the mere viral load data is subject to strong fluctuations and has limited value for political deciders on its own. Therefore, the state of Rhineland–Palatinate has commissioned the University Medical Center at Johannes Gutenberg University Mainz to conduct a representative cohort study called SentiSurv, in which an increasing number of up to 12,000 participants have been using sensitive antigen self-tests once or twice a week to test themselves for SARS-CoV-2 and report their status. This puts the state of Rhineland–Palatinate in the fortunate position of having time series of both, the viral load in wastewater and the prevalence of SARS-CoV-2 in the population. Our main contribution is a calibration study based on the data from 2023-01-08 until 2023-10-01 where we identified a scaling factor ( 0.208 ± 0.031 ) and a delay ( 5.07 ± 2.30 days) between the virus load in wastewater, normalized by the pepper mild mottle virus (PMMoV), and the prevalence recorded in the SentiSurv study. The relation is established by fitting an epidemiological model to both time series. We show how that can be used to estimate the prevalence when the cohort data is no longer available and how to use it as a forecasting instrument several weeks ahead of time. We show that the calibration and forecasting quality and the resulting factors depend strongly on how wastewater samples are normalized. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Recent research has demonstrated how epigenetic mechanisms regulate the host–virus interactions in COVID-19. It has also shown that microRNAs (miRNAs) are one of the three fundamental mechanisms of the epigenetic regulation of gene expression and play an important role in viral infections. A pilot study published by our research group identified, through next-generation sequencing (NGS), that miR-4433b-5p, miR-320b, and miR-16–2-3p are differentially expressed between patients with COVID-19 and controls. Thus, the objectives of this study were to validate the expression of these miRNAs using quantitative real-time polymerase chain reaction (qRT-PCR) and to perform in silico analyses. Patients with COVID-19 (n = 90) and healthy volunteers (n = 40) were recruited. MiRNAs were extracted from plasma samples and validated using qRT-PCR. In addition, in silico analyses were performed using mirPath v.3 software. MiR-320b was the only miRNA upregulated in the case group com-pared to the control group. The in silico analyses indicated the role of miR-320b in the regulation of the KITLG gene and consequently in the inflammatory process. This study confirmed that miR-320b can distinguish patients with COVID-19 from control participants; however, further research is needed to determine whether this miRNA can be used as a target or a biomarker. [ABSTRACT FROM AUTHOR]

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels. [ABSTRACT FROM AUTHOR]

Oral and maxillofacial trauma is influenced by various factors, including regional characteristics and social background. Due to the coronavirus disease 2019 (COVID-19) pandemic, a state of emergency was declared in Japan in March 2020. In this study, we aimed to examine the dynamics of patients with oral and maxillofacial trauma over a 12-years period using interrupted time-series (ITS) analysis. Patients were examined at the Shimane University Hospital, Maxillofacial Trauma Center from April 2012 to April 2023. In addition to general patient characteristics, data regarding the type of trauma and its treatment were obtained from 1203 patients (770 men and 433 women). Group comparisons showed significant differences in age, trauma status, method of treatment, referral source, route, and injury occasion. ITS analysis indicated significant changes in combined nasal fractures, non-invasive reduction, and sports injuries (P < 0.05), suggesting COVID-19 significantly impacted oral and maxillofacial trauma dynamics. A pandemic of an infectious disease may decrease the number of minor trauma cases but increase the number of injuries from outdoor activities, resulting in no overall change in the dynamics of the number of trauma patients. Medical systems for oral and maxillofacial trauma should be in place at all times, independent of infectious disease pandemics. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic continues to pose significant challenges worldwide, despite widespread vaccination. Researchers are actively exploring antiviral treatments to assess their efficacy against emerging virus variants. The aim of the study is to employ M-polynomial, neighborhood M-polynomial approach and QSPR/QSAR analysis to evaluate specific antiviral drugs including Lopinavir, Ritonavir, Arbidol, Thalidomide, Chloroquine, Hydroxychloroquine, Theaflavin and Remdesivir. Utilizing degree-based and neighborhood degree sum-based topological indices on molecular multigraphs reveals insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, surface tension, boiling point, enthalpy of vaporization, flash point, molar refraction and molar volume are crucial in predicting their efficacy against viruses. These properties influence the solubility, permeability, and bio availability of the drugs, which in turn affect their ability to interact with viral targets and inhibit viral replication. In QSPR analysis, molecular multigraphs yield notable correlation coefficients exceeding those from simple graphs: molar refraction (MR) (0.9860), polarizability (P) (0.9861), surface tension (ST) (0.6086), molar volume (MV) (0.9353) using degree-based indices, and flash point (FP) (0.9781), surface tension (ST) (0.7841) using neighborhood degree sum-based indices. QSAR models, constructed through multiple linear regressions (MLR) with a backward elimination approach at a significance level of 0.05, exhibit promising predictive capabilities highlighting the significance of the biological activity I C 50 (Half maximal inhibitory concentration). Notably, the alignment of predicted and observed values for Remdesivir's with obs p I C 50 = 6.01 ,pred p I C 50 = 6.01 ( p I C 50 represents the negative logarithm of I C 50 ) underscores the accuracy of multigraph-based QSAR analysis. The primary objective is to showcase the valuable contribution of multigraphs to QSPR and QSAR analyses, offering crucial insights into molecular structures and antiviral properties. The integration of physicochemical applications enhances our understanding of factors influencing antiviral drug efficacy, essential for combating emerging viral strains effectively. [ABSTRACT FROM AUTHOR]

While many studies have documented adverse impact of multiple chronic conditions or multimorbidity on COVID-19 outcomes in patients, there is scarcity of report on how physicians managed these patients. We investigated the experiences and challenges of clinicians in managing patients with multimorbidity throughout the COVID-19 pandemic in Odisha state, India. To understand the factors influencing illness management and the adaptive responses of physicians alongside the evolving pandemic, we followed a longitudinal qualitative study design. Twenty-three physicians comprising general practitioners, specialists, and intensivists, were telephonically interviewed in-depth. Saldana's longitudinal qualitative data analysis method was employed for data analysis. COVID-19 pandemic initially diverted the attention of health systems, resulting in reduced care. With time, the physicians overcame fear, anxiety, and feelings of vulnerability to COVID-19 and started prioritising patients with multimorbidity for treatment and vaccination. All physicians recommended teleconsultation and digital health records to benefit chronic illness care during future public health crises. The findings underscore the transformative potential of physician resilience and adaptation during the COVID-19 pandemic, emphasizing the importance of prioritizing patients with multimorbidity, incorporating teleconsultation, and implementing digital health records in healthcare systems to enhance chronic illness care and preparedness for future public health crises. [ABSTRACT FROM AUTHOR]

Danshen, a prominent herb in traditional Chinese medicine (TCM), is known for its potential to enhance physiological functions such as blood circulation, immune response, and resolve blood stasis. Despite the effectiveness of COVID-19 vaccination efforts, some individuals still face severe complications post-infection, including pulmonary fibrosis, myocarditis arrhythmias and stroke. This study employs a network pharmacology and molecular docking approach to investigate the potential mechanisms underlying the therapeutic effects of candidate components and targets from Danshen in the treatment of complications in COVID-19. Candidate components and targets from Danshen were extracted from the TCMSP Database, while COVID-19-related targets were obtained from Genecards. Venn diagram analysis identified common targets. A Protein–Protein interaction (PPI) network and gene enrichment analysis elucidated potential therapeutic mechanisms. Molecular docking evaluated interactions between core targets and candidate components, followed by molecular dynamics simulations to assess stability. We identified 59 potential candidate components and 123 targets in Danshen for COVID-19 treatment. PPI analysis revealed 12 core targets, and gene enrichment analysis highlighted modulated pathways. Molecular docking showed favorable interactions, with molecular dynamics simulations indicating high stability of key complexes. Receiver operating characteristic (ROC) curves validated the docking protocol. Our study unveils candidate compounds, core targets, and molecular mechanisms of Danshen in COVID-19 treatment. These findings provide a scientific foundation for further research and potential development of therapeutic drugs. [ABSTRACT FROM AUTHOR]

Respiratory diseases, including influenza and coronaviruses, pose recurrent global threats. This study delves into the respiratory surveillance systems, focusing on the effectiveness of SARI sentinel surveillance for total and severe cases incidence estimation. Leveraging data from the COVID-19 pandemic in Chile, we examined 2020–2023 data (a 159-week period) comparing census surveillance results of confirmed cases and hospitalizations, with sentinel surveillance. Our analyses revealed a consistent underestimation of total cases and an overestimation of severe cases of sentinel surveillance. To address these limitations, we introduce a nowcasting model, improving the precision and accuracy of incidence estimates. Furthermore, the integration of genomic surveillance data significantly enhances model predictions. While our findings are primarily focused on COVID-19, they have implications for respiratory virus surveillance and early detection of respiratory epidemics. The nowcasting model offers real-time insights into an outbreak for public health decision-making, using the same surveillance data that is routinely collected. This approach enhances preparedness for emerging respiratory diseases by the development of practical solutions with applications in public health. [ABSTRACT FROM AUTHOR]

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification. [ABSTRACT FROM AUTHOR]