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Loneliness and associated physical and cognitive health decline among the aging population is an important medical concern, exacerbated in times of abnormal isolation like the 2020–2021 Covid-19 pandemic lockdown. In this backdrop, recent "social prescribing" based health policy initiatives such as community groups as a support structure for the aging population assumes great importance. In this paper, we evaluate and quantify the impact of such social prescribing policies in combatting loneliness and related health degeneration of the aging population in times of abnormal isolation. To this end, we conduct a natural experiment across a sample of 618 individuals aged 65 and over with varying access to community groups during the Covid-19 lockdown period. Using a random-effects, probit model to compare the differences in health outcomes of participants with access to community groups (target) with those without access (control), we find that the target group was 2.65 times less likely to suffer from loneliness as compared to the control group, along with lower incidences of reported cardiovascular and cognitive health decline. These initial findings provide preliminary support in favor of the interventional power of social prescription tools in mitigating loneliness and its consequent negative health impact on the aging population. [ABSTRACT FROM AUTHOR]

A variety of reasons have made it more difficult for educators and tutors to anticipate students' performance. Numerous researchers have used various predictive models to identify students who may be at-risk of dropping out early. Additionally, these methods were used to forecast final semester grades based on various datasets. However, these prediction models still fall short of meeting educational management requirements. In this paper, we propose the deep learning (DL) based model named students academic performance prediction network (SAPPNet) to predict the students' grades. We consider the questionnaire-based Jordan University dataset which contains demographic information, usage of digital tools before and after COVID-19, sleep times before and after COVID-19, social interaction, psychological state, and academic performance. SAPPNet consists of spatial convolution modules which are designed to extract spatial dependencies includes categorical and numerical attributes that represent static features (gender, level/year, age, digital tools used before and after COVID-19, psychological condition using prolonged e-learning tools) and temporal module for temporal dependencies involves sequences that capture changes before and after COVID-19. Additionally, we also try to implement classical machine learning (ML) models including support vector machine, k nearest neighbor, decision tree, and random forest, and DL models named artificial neural network, convolutional neural network, long short-term memory, and students learning prediction network. Simulation results show that SAPPNet achieved the best performance compared to state-of-the-art methods, with an accuracy, precision, recall, and an F1-score of 93 % . The proposed model with spatial and temporal modules improves the prediction performance, and it implies new aspect of the educational dataset. [ABSTRACT FROM AUTHOR]

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests. [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]

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]

Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry. A total of 694,366 documents were retrieved. Filtering the articles using text mining yielded 1,554 oral health/dentistry papers. The list of articles was classified into five topics after applying a Latent Dirichlet Allocation (LDA) model. This classification was compared to the author's classification which yielded 17 categories. After a full-text review of articles in the category "Oral manifestations in patients with COVID-19", eight papers were selected to extract data. The most frequent oral manifestations were xerostomia (n = 405, 17.8%) and mouth pain or swelling (n = 289, 12.7%). These oral manifestations in patients with COVID-19 must be considered with other symptoms to diminish the risk of dentist-patient infection. [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]

In this paper, we discussed the impossibility of achieving zero-covid cases per day for all time with the help of fuzzy theory, while how a single case can trigger chaotic situation in the nearby city is elaborated using multi-scrolls. To accomplish this goal, we consider the number of new cases per day; x 1 to be the preferred state variable by restricting its value to the interval (0, 1). One can need to think of x 1 as a member of a fuzzy set and provide that set with appropriate membership functions. Moreover, how a single incident in one city can spread chaos to other cities is also addressed at length, using multi-scroll attractors and the signal excitation function. In addition, a bifurcation diagram of daily new instances vs the parameter α 1 is shown, elaborating that daily new cases may show a decrease under strict rules and regulations, but can again lead to chaos. Apart from biologist, this paper can play vital role for engineers as well in a sense that, a signal function can be embedded in non-symmetric systems for the creation of multi-scroll attractors in all directions using a generalized algorithm that has been designed in the current work. Finally, it is our future target to show that the covid is leading towards influenza and will be no more dangerous as was in the past. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic, besides generating health distress, influences the socio-economic conditions of humankind. Every adolescent's lifestyle is affected by the virus. Healthy adolescents are not only key contributors to the forthcoming workforce but also a source of a country's human capital. The purpose of the article is to examine the efficacy of various Asian countries in regulating the spread of the coronavirus among adolescents. In addition to that, the paper also attempts to pinpoint the prime causes of the inefficiency of a country in regulating the same. The paper also examines the behavioural changes among adolescents across Asian countries in pre-and-post pandemic times. In this context, the study identifies the impact of adolescents' tobacco consumption, female political leadership, and accreditation on a country's efficacy to regulate adolescents' coronavirus infection. The study's empirical analysis covers twenty-one Asian countries. By using the Panel Stochastic Production Frontier, the study concludes that Kazakhstan is the most efficient country and Afghanistan is the least efficient country on the list. The inefficiency effects estimates conclude that adolescents' tobacco consumption decreases and good governance practices increase the efficiency of a country in regulating the spread of adolescent coronavirus infection. Additionally, the paper finds no significant behavioural changes among adolescents in pre-and-post pandemic times across Asian countries. The paper concludes with appropriate policy recommendations supported by empirical evidence. The paper also identifies its shortcomings and suggests potential future lines of inquiry. [ABSTRACT FROM AUTHOR]

This study examines the relationship between respondents' vaccine hesitancy, reported media consumption patterns, ideological leanings, and trust in science. A large-scale survey conducted in the US in 2022 (N = 1,646) assessed self-reported COVID-19 vaccination, trust in science, and reported media consumption. Findings show that, regardless of personal ideology, individuals who consumed less conservative media and had a more ideologically diverse media diet were more likely to be fully vaccinated and boosted. Additionally, consuming more conservative media was negatively associated with trust in science, but this relationship was weaker among those with a more ideologically diverse media diet. By incorporating data from an earlier wave of the survey in the summer of 2020, before COVID-19 vaccines were available, we found that a less conservative and more ideologically diverse media diet in 2022 predicted vaccination behavior in 2022, controlling for prior vaccine intentions and media consumption in 2020. A similar survey conducted in the UK in the summer of 2020 paralleled patterns in the US regarding vaccine intentions and media consumption. These results suggest that an ideologically diverse media diet is associated with reduced vaccine hesitancy. Public health initiatives might benefit from encouraging ideologically diverse media consumption. [ABSTRACT FROM AUTHOR]

Stress hyperglycaemia is common in critical illness. We have previously observed that increasing severity of respiratory failure in patients with severe COVID-19 is associated with increased insulin demand. Given previously reported direct effects of hypoxia on insulin action, we reasoned that rapid improvements in oxygenation following prone positioning may improve insulin sensitivity and increase risk of hypoglycaemia. A retrospective multi-centre service evaluation comparing blood glucose and insulin administration in patients with COVID-19 pneumonitis receiving prone mechanical ventilation, comparing the 16 h pre-prone and 16 h post-prone time periods. 155 patients were included in this analysis. Oxygenation improved significantly following prone positioning (change in SpO2/FIO2 per hour prone: 3.01 ± 0.14, P < 0.0001). Glycaemic control was similar during the supine and prone study periods, and there were no hypoglycaemic events in the prone study period. Prone positioning was associated with an unexpected modest but significant increase in insulin requirements (mean difference in total insulin dose (IU): 8.32 ± 2.14, P < 0.001) that was robust to several sensitivity analyses, and could not be explained by changes in carbohydrate intake. We did not observe an increased rate of hypoglycaemia during prone ventilation and the adequacy of glycaemic control was comparable during the supine and prone study periods. Unexpectedly, prone ventilation was associated with an increase in insulin requirements despite significant improvement in hypoxaemia. Our findings support the safety of prone ventilation with respect to glycaemic control and identify a novel relationship between ventilation position and insulin requirements in critical illness. [ABSTRACT FROM AUTHOR]

When using the reverse-transcription quantitative polymerase chain reaction (RT-qPCR) technique for quantitative assessment of microRNA (miRNA) expression, normalizing data using a stable endogenous gene is essential; however, no universally adequate reference gene exists. Therefore, in this study, we aimed to determine, via the RNA-Seq technique, the most adequate endogenous normalizer for the expression assessment of plasma miRNAs in patients with coronavirus disease 2019 (COVID-19). Two massive sequencing procedures were performed (a) to identify differentially expressed miRNAs between patients with COVID-19 and healthy volunteers (n = 12), and (b) to identify differentially expressed miRNAs between patients with severe COVID-19 and those with mild COVID-19 (n = 8). The endogenous normalizer candidates were selected according to the following criteria: (1) the miRNA must have a fold regulation = 1; (2) the miRNA must have a p-value > 0.990; and (3) the miRNAs that were discovered the longest ago should be selected. Four miRNAs (hsa-miR-34a-3p, hsa-miR-194-3p, hsa-miR-17-3p, and hsa-miR-205-3p) met all criteria and were selected for validation by RT-qPCR in a cohort of 125 patients. Of these, only hsa-miR-205-3p was eligible endogenous normalizers in the context of COVID-19 because their expression was stable between the compared groups. [ABSTRACT FROM AUTHOR]

To evaluate the COVID-19 infection risk and the effectiveness of countermeasures at mass-gathering events, we measured the dispersion and advective diffusion of artificial droplets and artificial droplet nuclei at the Tokyo Dome, Japan (capacity 55,000 people). We also measured and evaluated the effectiveness of wearing masks and increasing the space between seating areas. If people were seated facing forward, artificial droplets did not reach the mouths of surrounding people, suggesting low risk of droplet transmission. For an artificially generated cough or sneeze, the volume of droplets deposited on the hair, back of the neck, and back of the human in front, and the backs of the seats in front, decreased by two to three orders of magnitude when a mask was worn, regardless of the type of mask. However, when the mask was worn with the nose out, the amount deposited on the back of the seat in front was reduced by only 17%. Even in seats with the highest particle concentration in the vicinity of the source, only 0.097%–0.24% of the generated droplet nuclei (1.0–3.0 μm) from the source were inhaled. Our results suggest that the infection risk at the Tokyo Dome via droplet and airborne transmission was low. [ABSTRACT FROM AUTHOR]

Most published prediction models for Coronavirus Disease 2019 (COVID-19) were poorly reported, at high risk of bias, and heterogeneous in model performance. To tackle methodological challenges faced in previous prediction studies, we investigated whether model updating and extending improves mortality prediction, using the Intensive Care Unit (ICU) as a proxy. All COVID-19 patients admitted to seven ICUs in the Euregio-Meuse Rhine during the first pandemic wave were included. The 4C Mortality and SEIMC scores were selected as promising prognostic models from an external validation study. Five predictors could be estimated based on cohort size. TRIPOD guidelines were followed and logistic regression analyses with the linear predictor, APACHE II score, and country were performed. Bootstrapping with backward selection was applied to select variables for the final model. Additionally, shrinkage was performed. Model discrimination was displayed as optimism-corrected areas under the ROC curve and calibration by calibration slopes and plots. The mortality rate of the 551 included patients was 36%. Discrimination of the 4C Mortality and SEIMC scores increased from 0.70 to 0.74 and 0.70 to 0.73 and calibration plots improved compared to the original models after updating and extending. Mortality prediction can be improved after updating and extending of promising models. [ABSTRACT FROM AUTHOR]

After the COVID-19 pandemic, fever clinics urgently require rapid nucleic acid tests to enhance their capacity for timely pathogen detection. This study evaluated the analytical performance and clinical utility of the Flash10 SARS-CoV-2 point-of-care test (Flash10 POCT) for detecting SARS-CoV-2 in patients with fever in the adult fever clinic in Beijing Tsinghua Changgung Hospital from August 1 to August 30, 2023. The analytical performance and clinical utility of the Flash10 POCT for detecting SARS-CoV-2 were assessed in 125 patients with fever syndrome in the adult fever clinic. The Flash10 POCT demonstrated an analytical precision of 3.1% for the Ct values of the ORF1ab gene and 2.9% for the Ct values of the N gene in SARS-CoV-2 nucleic acid testing. Furthermore, the Flash10 POCT demonstrated a lower limit of detection (LoD) of 100 copies/mL, with no detected aerosol contamination leakage. Of the 125 patients (median age 61.9 years, 52% male and 48% female), both the Flash10 POCT and RT-PCR tests yielded positive results for 100 patients and negative results for 25 patients (Fisher's exact test, p < 0.0001). The median turn-around-time for the Flash10 POCT was significantly shorter, at 1.05 h, compared to 16.15 h required for RT-PCR tests (Wilcoxon signed rank test, p < 0.0001). The Flash10 POCT showed high analytical performance, achieving a 100% detection rate for SARS-CoV-2 compared to RT-PCR tests, while also exhibiting a significantly shorter turn-around-time. Implementing the Flash10 POCT had the potential to expedite the care of adults presenting with fever. [ABSTRACT FROM AUTHOR]

Intertemporal decisions are crucial to survival, especially when humans are exposed to ecological threats. However, it remains unclear whether and how pandemic threats impact intertemporal choices across cultures. We conducted two studies in two cultural contexts (N = 1180). Study 1a found that Chinese who perceived more pandemic threats showed higher temporal discounting rates (i.e., preferred immediate smaller over delayed larger rewards), and this relationship was mediated by negative emotions. Study 1b, using threat priming, revealed that Chinese participants primed with a high-threat condition (depicting a pandemic threat) showed higher temporal discounting rates compared to those primed with a low-threat condition (describing the flu). Differently, Study 2a revealed that perceived pandemic threat levels of Americans did not directly predict temporal discounting rates. Using similar threat priming, Study 2b further confirmed that Americans in the high-threat priming condition showed no significant differences in temporal discounting rates compared to those in the low-threat priming condition. The current research deepens the understanding of the cultural difference in the impact of pandemic threats on intertemporal decision-making and further informs the development of interventions that promote individual future-oriented thinking in the face of pandemics. [ABSTRACT FROM AUTHOR]

Between 2020 and 2022, lockdown and restraining measures as a result of the COVID-19 pandemic led to changes in daily routines and also in language usage. At certain points during the pandemic, populations in some countries were unwilling or unlikely to respond to government messages, either because of the tone and analytic discourse used by leaders, or because they did not understand the messages. Linguistic markers and meanings were therefore linked to low levels of engagement, negative emotions and high levels of analytical thinking, especially in relation to the discourses of influential international leaders. We subjected sixteen speeches by eight country leaders to topic modelling and sentiment analysis in order to understand how the psychological functions of language were affected during two different periods of the COVID-19 pandemic. In this topic analysis we organize 39,073 words collected from sixteen authentic speeches delivered in two different periods of the acute phase of the pandemic. These were encoded in the Linguistic Inquiry Word Count program (LIWC), with the main aim being to identify differences between the periods. We examined the following aspects: (1) the emotional tone, analytical thinking and clout (empathy dimension); (2) the changes in these three dimensions or factors between periods 1 and 2 (February and May 2020). We observed a negative relationship between emotional tone and analytical thinking and a positive relationship between clout and emotional tone. When we considered the changes in pandemic circumstances, the psycholinguistic profiles of eight country leaders demonstrated fluctuations in language and emotions. Further reviews and research should focus on the current language and deficit wording of this population (leaders). We also note that psychologists and schoolteachers can play an important role in supporting language programmes with positive wording and by emphasising the collateral effects of face-to-face classes when teaching children to read and write. [ABSTRACT FROM AUTHOR]

While Coronavirus disease 2019 (COVID-19) vaccines have proven to be both effective and generally safe, rare but severe adverse events following immunization (AEFIs) are described. Autoantibodies to platelet factor-4 are associated with catastrophic thrombotic AEFIs, but comprehensive investigations of other autoantibodies are lacking. We aimed to detect and describe autoantibodies targeting coagulation-related proteins in a population-wide cohort (SWEDEGENE) including AEFIs attributed to COVID-19 vaccines in Sweden. Subjects were recruited from December 2020 to October 2022 and were stratified based on diagnosis and COVID-19 exposure. Screening was carried out in two phases, with a multiplex bead-based assay in the first subset (until September 2021) and with targeted assays for the second (until October 2022). Positivity was defined based on absolute, relative, and biological/technical thresholds. Patients with coagulation-related AEFIs were older and the Vaxzevria vaccine was overrepresented in this group. Two cases had antiphospholipid antibodies but none had PF4 antibodies. We identified six positives for protein S autoantibodies. Protein S concentrations were negatively correlated with autoantibody response in patients with immunoreactivity and functional analysis revealed low protein S activity in three subjects. Our population-wide analysis reveals cases with autoantibodies against protein S which possibly underlie coagulopathic AEFIs. [ABSTRACT FROM AUTHOR]

The far-UVC (222 nm) system has emerged as a solution for controlling airborne transmission, yet its effect on indoor air quality, particularly concerning positioning, remains understudied. In this study, we examined the impact of far-UVC lamp position on the disinfection and secondary contaminant formation in a small office. We employed a three-dimensional computational fluid dynamics (CFD) model to integrate UV intensity fields formed by different lamp positions (ceiling-mounted, wall-mounted, and stand-alone types) along with the air quality model. Our findings reveal that the ceiling-mounted type reduces human exposure to airborne pathogens by up to 80% compared to scenarios without far-UVC. For all the lamp positions, O3 concentration in the breathing zone increases by 4–6 ppb after one hour of operation. However, it should be noted that a high concentration zone (> 25 ppb) forms near the lamp when it is turned on. Moreover, ventilation plays a crucial role in determining human exposure to airborne pathogens and secondary contaminants. Increasing the ventilation rate from 0.7 h−1 to 4 h−1 reduces airborne pathogen and secondary contaminant concentrations by up to 90%. However, caution is warranted as higher ventilation rates can lead to elevated O3 indoors, especially under conditions of high outdoor O3 concentrations. [ABSTRACT FROM AUTHOR]

Some studies have identified influencing factors of COVID-19 illness in elderly, such as underlying diseases, but research on the effect of nutritional status is still lacking. This study retrospectively examined the influence of nutritional status on the outcome of elderly COVID-19 inpatients. A retrospective analysis of the clinical data of 4241 COVID-19 patients who were admitted to a third-class hospital of Nanchang between November 1, 2022 and January 31, 2023 was conducted. Nutritional status was assessed using the prognostic nutritional index (PNI) and controlling nutritional status score (CONUT). The influence of nutritional status on the outcome of COVID-19 patients was determined through multivariate adjustment analysis, restrictive cubic spline, and receiver operating characteristic curve (ROC). Compared with mild/no malnutrition, severe malnutrition substantially increased the critical outcome of COVID-19. A linear relationship was observed between the odds ratio (OR) and PNI and CONUT (P > 0.05). The area under the ROC curve indicated that PNI was the better predictor. The optimal cutoff value of PNI was 38.04 (95%CI: 0.797 ~ 0.836, AUC = 0.817), with a sensitivity of 70.7% and a specificity of 79.6%. The critical illness of elderly COVID-19 patients shows a linear relationship with malnutrition at admission. The use of PNI to assess the prognosis of COVID-19 eldeely patients is reliable, highlighting the importance for doctors to closely pay attention to the nutritional status of COVID-19 patients. Focusing on nutritional status in clinical practice can effectively reduce the critical illness of elderly COVID-19 patients. [ABSTRACT FROM AUTHOR]

The effects of the COVID-19 pandemic on comprehensive maternal deaths in Brazil have not been fully explored. Using publicly available data from the Brazilian Mortality Information (SIM) and Information System on Live Births (SINASC) databases, we used two complementary forecasting models to predict estimates of maternal mortality ratios using maternal deaths (MMR) and comprehensive maternal deaths (MMRc) in the years 2020 and 2021 based on data from 2008 to 2019. We calculated national and regional standardized mortality ratio estimates for maternal deaths (SMR) and comprehensive maternal deaths (SMRc) for 2020 and 2021. The observed MMRc in 2021 was more than double the predicted MMRc based on the Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per 100,000 live births, respectively). We found persisting sub-national variation in comprehensive maternal mortality: SMRc ranged from 1.74 (95% confidence interval [CI] 1.64, 1.86) in the Northeast to 2.70 (95% CI 2.45, 2.96) in the South in 2021. The observed national estimates for comprehensive maternal deaths in 2021 were the highest in Brazil in the past three decades. Increased resources for prenatal care, maternal health, and postpartum care may be needed to reverse the national trend in comprehensive maternal deaths. [ABSTRACT FROM AUTHOR]

Vaccination has played a critical role in mitigating COVID-19. Despite the availability of licensed vaccines, there remains a pressing need for improved vaccine platforms that provide high protection, safety, and versatility, while also reducing vaccine costs. In response to these challenges, our aim is to create a self-adjuvanted vaccine against SARS-CoV-2, utilizing Virus-Like Particles (VLPs) as the foundation. To achieve this, we produced bacteriophage (Qβ) VLPs in a prokaryotic system and purified them using a rapid and cost-effective strategy involving organic solvents. This method aims to solubilize lipids and components of the cell membrane to eliminate endotoxins present in bacterial samples. For vaccine formulation, Receptor Binding Domain (RBD) antigens were conjugated using chemical crosslinkers, a process compatible with Good Manufacturing Practice (GMP) standards. Transmission Electron Microscopy (TEM) confirmed the expected folding and spatial configuration of the QβVLPs vaccine. Additionally, vaccine formulation assessment involved SDS-PAGE stained with Coomassie Brilliant Blue, Western blotting, and stereomicroscopic experiments. In vitro and in vivo evaluations of the vaccine formulation were conducted to assess its capacity to induce a protective immune response without causing side effects. Vaccine doses of 20 µg and 50 µg stimulated the production of neutralizing antibodies. In in vivo testing, the group of animals vaccinated with 50 µg of vaccine formulation provided complete protection against virus infection, maintaining stable body weight without showing signs of disease. In conclusion, the QβVLPs-RBD vaccine has proven to be effective and safe, eliminating the necessity for supplementary adjuvants and offering a financially feasible approach. Moreover, this vaccine platform demonstrates flexibility in targeting Variants of Concern (VOCs) via established conjugation protocols with VLPs. [ABSTRACT FROM AUTHOR]

Patients with coronavirus disease 2019 (COVID-19) often experience acute kidney injury, linked to disease severity or mortality, along with renal tubular dysfunction and megalin loss in proximal tubules. Megalin plays a crucial role in kidney vitamin D metabolism. However, the impact of megalin loss on vitamin D metabolism during COVID-19 is unclear. This study investigated whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection reduces megalin expression in proximal tubules and its subsequent effect on vitamin D metabolism in mice expressing human angiotensin converting enzyme 2 (K18-hACE2 mice). Histological and immunohistochemical staining analyses revealed glomerular and capillary congestion, and elevated renal neutrophil gelatinase-associated lipocalin levels, indicative of acute kidney injury in K18-hACE2 mice. In SARS-CoV-2-infected mice, immunohistochemical staining revealed suppressed megalin protein levels. Decreased vitamin D receptor (VDR) localization in the nucleus and increased mRNA expression of VDR, CYP27B1, and CYP24A1 were observed by quantitative PCR in SARS-CoV-2-infected mice. Serum vitamin D levels remained similar in infected and vehicle-treated mice, but an increase in tumor necrosis factor-alpha and a decrease in IL-4 mRNA expression were observed in the kidneys of the SARS-CoV-2 group. These findings suggest that megalin loss in SARS-CoV-2 infection may impact the local role of vitamin D in kidney immunomodulation, even when blood vitamin D levels remain unchanged. [ABSTRACT FROM AUTHOR]

Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors. [ABSTRACT FROM AUTHOR]

The effects of the COVID-19 pandemic on comprehensive maternal deaths in Brazil have not been fully explored. Using publicly available data from the Brazilian Mortality Information (SIM) and Information System on Live Births (SINASC) databases, we used two complementary forecasting models to predict estimates of maternal mortality ratios using maternal deaths (MMR) and comprehensive maternal deaths (MMRc) in the years 2020 and 2021 based on data from 2008 to 2019. We calculated national and regional standardized mortality ratio estimates for maternal deaths (SMR) and comprehensive maternal deaths (SMRc) for 2020 and 2021. The observed MMRc in 2021 was more than double the predicted MMRc based on the Holt-Winters and autoregressive integrated moving average models (127.12 versus 60.89 and 59.12 per 100,000 live births, respectively). We found persisting sub-national variation in comprehensive maternal mortality: SMRc ranged from 1.74 (95% confidence interval [CI] 1.64, 1.86) in the Northeast to 2.70 (95% CI 2.45, 2.96) in the South in 2021. The observed national estimates for comprehensive maternal deaths in 2021 were the highest in Brazil in the past three decades. Increased resources for prenatal care, maternal health, and postpartum care may be needed to reverse the national trend in comprehensive maternal deaths. [ABSTRACT FROM AUTHOR]

COVID-19 is a severe respiratory disease affecting millions worldwide, causing significant morbidity and mortality. Adrenomedullin (bio-ADM) is a vasoactive hormone regulating the endothelial barrier and has been associated with COVID-19 mortality and other adverse events. This prospective cohort pilot study included 119 consecutive patients with verified SARS-CoV-2 infection admitted to two intensive care units (ICUs) in Southern Sweden. Bio-ADM was retrospectively analysed from plasma on ICU admission, and days 2 and 7. Information on comorbidities, adverse events and mortality was collected. The primary outcome was 90-day mortality, and secondary outcomes were markers of disease severity. The association between bio-ADM and outcomes was analysed using survival analysis and logistic regression. Bio-ADM on admission, day 2, and day 7 only moderately predicted 90-day mortality in univariate and multivariate Cox regression. The relative change in bio-ADM between sample times predicted 90-day mortality better even when adjusting for the SAPS3 score, with an HR of 1.09 (95% CI 1.04–1.15) and a C-index of 0.82 (95% CI 0.72–0.92) for relative change between day 2 and day 7. Bio-ADM had a good prediction of the need for renal replacement therapy in multivariate Cox regression adjusting for creatinine, where day 2 bio-ADM had an HR of 3.18 (95% CI 1.21–8.36) and C-index of 0.91 (95% CI 0.87–0.96). Relative changes did not perform better, possibly due to a small sample size. Admission and day 2 bio-ADM was associated with early acute kidney injury (AKI). Bio-ADM on ICU admission, day 2 and day 7 predicted 90-day mortality and dialysis needs, highlighting bio-ADM's importance in COVID-19 pathophysiology. Bio-ADM could be used to triage patients with a risk of adverse outcomes and as a potential target for clinical interventions. [ABSTRACT FROM AUTHOR]

To identify the differences between COVID-19-associated and non-COVID-19-associated olfactory dysfunction (OD), we analyzed demographic and clinical characteristics based on the causative virus (COVID versus non-COVID groups) in patients with post-infectious olfactory dysfunction (PIOD) who underwent the olfactory questionnaire and olfactory function test. Out of 169 patients with PIOD, 99 were diagnosed with COVID-19 (COVID group), while 70 were not (non-COVID group). The COVID group was younger and had a higher percentage of male patients as well as patients with parosmia than the non-COVID group. In the initial olfactory function tests, the TDI, discrimination and identification scores were significantly higher in the COVID group than in the non-COVID group. TDI scores were significantly increased in patients with PIOD after treatment, regardless of the group. The threshold score was significantly increased by 1.38 in the COVID group while the identification score was significantly increased by 2.67 in the non-COVID group. Patients with COVID-19-associated OD were younger in age, tended to be male, had a higher incidence of parosmia, and had better initial olfactory function test results compared to those with non-COVID-19-associated OD. Following treatment, odor detection threshold improved in the COVID group, whereas odor identification improved in the non-COVID group. [ABSTRACT FROM AUTHOR]

Golden Syrian hamsters (Mesocricetus auratus) are a well-established animal model for human infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to their susceptibility to SARS-CoV-2 infection, robust virus replication and pathological manifestations similar to human COVID-19 pneumonia. To investigate the physiological changes upon infection in this animal model, we explored the alterations in daily energy expenditure (DEE), water turnover, body mass, body temperature, and locomotor activity in non-infected and SARS-CoV-2 infected Golden Syrian hamsters for four days post SARS-CoV-2. DEE was measured using the doubly labelled water method, which allows for the accurate estimation of carbon dioxide production and, consequently, energy expenditure in animals. Additionally, we investigated total water intake (TWI), which comprises drinking water, preformed water in food, and metabolic water. Using intraperitoneally implanted data loggers, we also monitored body core temperature and locomotor activity in some of the animals. Here we provide evidence for infected hamsters exhibiting significantly lower DEE and TWI compared to non-infected animals. We also observed an increase in body weight in the non-infected animals, while infected animals experienced weight loss. Further, infected animals showed a significantly decreased body temperature, indicating a generally lowered metabolic rate. [ABSTRACT FROM AUTHOR]

Mental health issues have increased substantially since the onset of the COVID-19 pandemic. However, health policymakers do not have adequate data and tools to predict mental health demand, especially amid a crisis. Using time-series data collected in Singapore, this study examines if and how algorithmically measured emotion indicators from Twitter posts can help forecast emergency mental health needs. We measured the mental health needs during 549 days from 1 July 2020 to 31 December 2021 using the public's daily visits to the emergency room of the country's largest psychiatric hospital and the number of users with "crisis" state assessed through a government-initiated online mental health self-help portal. Pairwise Granger-causality tests covering lag length from 1 day to 5 days indicated that forecast models using Twitter joy, anger and sadness emotions as predictors perform significantly better than baseline models using past mental health needs data alone (e.g., Joy Intensity on IMH Visits, χ2 = 14·9, P < ·001***; Sadness Count on Mindline Crisis, χ2 = 4·6, P = ·031*, with a one-day lag length). The findings highlight the potential of new early indicators for tracking emerging public mental health needs. [ABSTRACT FROM AUTHOR]

In this paper, we discussed the impossibility of achieving zero-covid cases per day for all time with the help of fuzzy theory, while how a single case can trigger chaotic situation in the nearby city is elaborated using multi-scrolls. To accomplish this goal, we consider the number of new cases per day; x 1 to be the preferred state variable by restricting its value to the interval (0, 1). One can need to think of x 1 as a member of a fuzzy set and provide that set with appropriate membership functions. Moreover, how a single incident in one city can spread chaos to other cities is also addressed at length, using multi-scroll attractors and the signal excitation function. In addition, a bifurcation diagram of daily new instances vs the parameter α 1 is shown, elaborating that daily new cases may show a decrease under strict rules and regulations, but can again lead to chaos. Apart from biologist, this paper can play vital role for engineers as well in a sense that, a signal function can be embedded in non-symmetric systems for the creation of multi-scroll attractors in all directions using a generalized algorithm that has been designed in the current work. Finally, it is our future target to show that the covid is leading towards influenza and will be no more dangerous as was in the past. [ABSTRACT FROM AUTHOR]

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.e. the additional mortality risk that COVID-19 ICU hospitalizations generate on non-COVID-19 patients receiving intensive care. The data contain the universe of ICU hospitalizations in SUS from February 26, 2020 to December 31, 2021. Spillover estimates are obtained from high-dimensional fixed effects regression models that control for a number of unobservable confounders. Our findings indicate that, on average, the pandemic increased the mortality risk of non-COVID-19 ICU patients by 1.296 percentage points, 95% CI 1.145–1.448. The spillover mortality risk is larger for non-COVID patients receiving intensive care due to diseases of the respiratory system, diseases of the skin and subcutaneous tissue, and infectious and parasitic diseases. As of July 2023, the WHO reports more than 6.9 million global deaths due to COVID-19 infection. However, our estimates of spillover effects suggest that the pandemic's total death toll is much higher. [ABSTRACT FROM AUTHOR]

Nowadays, global public health crises are occurring more frequently, and accurate prediction of these diseases can reduce the burden on the healthcare system. Taking COVID-19 as an example, accurate prediction of infection can assist experts in effectively allocating medical resources and diagnosing diseases. Currently, scholars worldwide use single model approaches or epidemiology models more often to predict the outbreak trend of COVID-19, resulting in poor prediction accuracy. Although a few studies have employed ensemble models, there is still room for improvement in their performance. In addition, there are only a few models that use the laboratory results of patients to predict COVID-19 infection. To address these issues, research efforts should focus on improving disease prediction performance and expanding the use of medical disease prediction models. In this paper, we propose an innovative deep learning model Whale Optimization Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) and Artificial Neural Network (ANN) called WOCLSA which incorporates three models ANN, CNN and LSTM. The WOCLSA model utilizes the Whale Optimization Algorithm to optimize the neuron number, dropout and batch size parameters in the integrated model of ANN, CNN and LSTM, thereby finding the global optimal solution parameters. WOCLSA employs 18 patient indicators as predictors, and compares its results with three other ensemble deep learning models. All models were validated with train-test split approaches. We evaluate and compare our proposed model and other models using accuracy, F1 score, recall, AUC and precision metrics. Through many studies and tests, our results show that our prediction models can identify patients with COVID-19 infection at the AUC of 91%, 91%, and 93% respectively. Other prediction results achieve a respectable accuracy of 92.82%, 92.79%, and 91.66% respectively, f1-score of 93.41%, 92.79%, and 92.33% respectively, precision of 93.41%, 92.79%, and 92.33% respectively, recall of 93.41%, 92.79%, and 92.33% respectively. All of these exceed 91%, surpassing those of comparable models. The execution time of WOCLSA is also an advantage. Therefore, the WOCLSA ensemble model can be used to assist in verifying laboratory research results and predict and to judge various diseases in public health events. [ABSTRACT FROM AUTHOR]

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots. [ABSTRACT FROM AUTHOR]

While the direct effects of the pandemic are well documented, less is known about the indirect ones, including changes in healthcare provision or human behavior. This paper aims to study the impact of indirect consequences on mortality, focusing on two leading causes (cardiovascular diseases, COVID-19) and places of death in Czechia, during the COVID-19 pandemic, one of the most severely affected European countries. The analysis was performed using data from the Czech Statistical Office and the Institute of Health Information and Statistics. The study compares annual mortality changes during three time periods: pre-pandemic (2018–2019), pandemic beginning and peaking (2020–2021), and pandemic fading (2022). Pandemic years were covered by the WHO public health emergency of international concern. Abridged life tables were computed, and Pollard's decomposition was used to calculate the contributions of causes and places of death on annual differences in life expectancy. Seasonal decomposition of monthly time series revealed an increase in cardiovascular mortality at home or in social care facilities corresponding to limitations in healthcare. While COVID-19 had a systemic negative effect on life expectancy during the pandemic, the impact of cardiovascular mortality according to place of death changed over time. This study contributes to the evidence base of systemic risks during health crises and emergency response. [ABSTRACT FROM AUTHOR]

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods – dynamic network markers (DNMs) and network analysis-based methods – as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used. [ABSTRACT FROM AUTHOR]

A dramatic increase in the number of outbreaks of dengue has recently been reported, and climate change is likely to extend the geographical spread of the disease. In this context, this paper shows how a neural network approach can incorporate dengue and COVID-19 data as well as external factors (such as social behaviour or climate variables), to develop predictive models that could improve our knowledge and provide useful tools for health policy makers. Through the use of neural networks with different social and natural parameters, in this paper we define a Correlation Model through which we show that the number of cases of COVID-19 and dengue have very similar trends. We then illustrate the relevance of our model by extending it to a Long short-term memory model (LSTM) that incorporates both diseases, and using this to estimate dengue infections via COVID-19 data in countries that lack sufficient dengue data. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic presents a serious threat to people's health, resulting in over 250 million confirmed cases and over 5 million deaths globally. To reduce the burden on national health care systems and to mitigate the effects of the outbreak, accurate modelling and forecasting methods for short- and long-term health demand are needed to inform government interventions aiming at curbing the pandemic. Current research on Covid-19 is typically based on a single source of information, specifically on structured historical pandemic data. Other studies are exclusively focused on unstructured online retrieved insights, such as data available from social media. However, the combined use of structured and unstructured information is still uncharted. This paper aims at filling this gap, by leveraging historical and social media information with a novel data integration methodology. The proposed approach is based on vine copulas, which allow us to exploit the dependencies between different sources of information. We apply the methodology to combine structured datasets retrieved from official sources and a big unstructured dataset of information collected from social media. The results show that the combined use of official and online generated information contributes to yield a more accurate assessment of the evolution of the Covid-19 pandemic, compared to the sole use of official data. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of artificial intelligence, the importance of multimodal sentiment analysis has never been more evident, especially amid the ongoing COVID-19 pandemic. Our research addresses the critical need to understand public sentiment across various dimensions of this crisis by integrating data from multiple modalities, such as text, images, audio, and videos sourced from platforms like Twitter. Conventional methods, which primarily focus on text analysis, often fall short in capturing the nuanced intricacies of emotional states, necessitating a more comprehensive approach. To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization Algorithm for effective feature fusion. Remarkably, our model achieves an impressive accuracy rate of 97.8%, outperforming existing approaches in the field. Additionally, by integrating the GeoCoV19 dataset, we facilitate a comprehensive analysis that spans linguistic and geographical boundaries, enriching our understanding of global pandemic discourse and providing critical insights for informed decision-making in public health crises. Through this holistic approach and innovative techniques, our research significantly advances multimodal sentiment analysis, offering a robust framework for deciphering the complex interplay of emotions during unprecedented global challenges like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 outcomes are less severe in women than men suggesting that female sex is protective. The steroids estradiol (E2) and progesterone (P4) promote anti-inflammatory immune responses and their therapeutic use for COVID-19 has been under investigation. The aim of the study was to evaluate the efficacy of a short systemic E2 and P4 combination in mitigating COVID-19 severity in hospitalized men and women. In a phase 2, single center, double blind, randomized placebo-controlled trial, ten male and female participants hospitalized for COVID-19 with scores 3–5 on the 9-point WHO ordinal scale were randomized to receive either (1) E2 cypionate (5 mg, IM) and micronized P4 (200 mg, PO), or (2) placebo-equivalent, in addition to standard of care (SOC). The primary outcome was the proportion of patients whose WHO scores improved to 1–2 on the day of discharge. Secondary outcomes included length of hospital stay (LOS), days on oxygen therapy (DOT), readmission rates (RR), adverse events (AEs), and change in circulating biomarkers using untargeted proteomics and cytokine profiling. There were no significant changes between the groups in primary outcome, LOS, DOT, RR or AEs. The E2P4 group exhibited a decrease in biomarker pathways of respiratory and gastrointestinal disease inflammation, infection by coronavirus, and immune cell trafficking and inflammatory response. A short-term E2P4 treatment in patients hospitalized for COVID-19 decreases biomarkers of inflammation. Considering the availability, low cost, and safety of E2 and P4, our results warrant additional studies to explore their effects in mitigating other viral pandemics. Clinical Trial Registration NCT04865029, ClinicalTrials.gov; (First trial registration 29/04/2021). [ABSTRACT FROM AUTHOR]

We analyzed data from positively tested COVID-19 outpatients to describe self-medication with OTC drugs and use of other remedies against symptoms of SARS-CoV-2 infection. We specifically considered their type and frequency, as well as associations with patient characteristics, and reasons for use. Data were collected between May 1, 2020 and February 22, 2021 with two questionnaires in an observational cohort study with PCR-confirmed SARS-CoV-2-positive adult outpatients in the district of Western Pomerania in Germany. 523 out of 710 outpatients (74%; 340 women and 183 men) reported using drugs and other remedies to relieve COVID-19-symptoms. Overall, participants reported utilization of 1282 finished dosage products or remedies, including 213 different ingredients. In the population of 710 outpatients, utilization of ibuprofen (26%), acetaminophen (21%), metamizole (14%), and acetylsalicylic acid (10%) was most commonly reported. Phytopharmaceuticals, herbal and animal products as well as vitamins and minerals were also frequently reported. Among the 523 participants who used drugs and other remedies, most commonly mentioned reasons for use were headache (40%), other kinds of pain (e.g. myalgia; 37%), fever (24%) and cough (16%). Our analysis showed that a majority of the participants tried to alleviate COVID-19-symptoms using drugs and other remedies. Especially analgesic and antipyretic agents, followed by herbal medicines, were used very frequently. Trial registration: German Register for Clinical Studies DRKS00021672, first registration on December 1st, 2020. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has critical implications for mental health in children. This study examined how the COVID-19 lockdown affected sleep duration and anxiety symptoms in Chinese school-aged children and the bidirectional association between sleep and anxiety before and during the COVID-19 lockdown. A school-based longitudinal cohort study was conducted to examine the relationship between sleep duration and anxiety scores before and after the COVID-19 lockdown from January to May 2020. Generalized estimating equations model was used to identify variables that contributed to the changes in sleep duration. The bidirectional relationship between sleep duration and anxiety symptoms was explored by cross-lagged analysis. 7681 children completed two waves of surveys were included in the analysis. The daily exercise duration, anxiety symptoms, and sleep duration decreased significantly during the lockdown compared with that before the lockdown. Based on generalized estimating equations model, older age, secondary school, and higher anxiety scores of participants were positively associated with sleep duration, while female and no COVID-19 infection history were negatively associated with it. Cross-lagged analysis showed higher anxiety score of children before the lockdown was significantly associated with shorter sleep duration during the lockdown; and shorter sleep duration of children before the lockdown was also significantly associated with a higher anxiety score during the lockdown. Under the context of the COVID-19 pandemic, there were longitudinal, bidirectional associations between children’s anxiety symptoms and sleep duration. For school students, mental health services and sleep education should be considered in the daily health education curriculum. [ABSTRACT FROM AUTHOR]