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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]

The SARS-CoV-2 coronavirus emerged in 2019 causing a COVID-19 pandemic that resulted in 7 million deaths out of 770 million reported cases over the next 4 years. The global health emergency called for unprecedented efforts to monitor and reduce the rate of infection, pushing the study of new diagnostic methods. In this paper, we introduce a cheap, fast, and non-invasive COVID-19 detection system, which exploits only exhaled breath. Specifically, provided an air sample, the mass spectra in the 10–351 mass-to-charge range are measured using an original micro and nano-sampling device coupled with a high-precision spectrometer; then, the raw spectra are processed by custom software algorithms; the clean and augmented data are eventually classified using state-of-the-art machine-learning algorithms. An uncontrolled clinical trial was conducted between 2021 and 2022 on 302 subjects who were concerned about being infected, either due to exhibiting symptoms or having recently recovered from illness. Despite the simplicity of use, our system showed a performance comparable to the traditional polymerase-chain-reaction and antigen testing in identifying cases of COVID-19 (that is, 95% accuracy, 94% recall, 96% specificity, and 92% -score). In light of these outcomes, we think that the proposed system holds the potential for substantial contributions to routine screenings and expedited responses during future epidemics, as it yields results comparable to state-of-the-art methods, providing them in a more rapid and less invasive manner. [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]

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]

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]

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]

The ongoing SARS-CoV-2 pandemic was initially managed by non-pharmaceutical interventions such as diagnostic testing, isolation of positive cases, physical distancing and lockdowns. The advent of vaccines has provided crucial protection against SARS-CoV-2. Neutralising antibody (nAb) responses are a key correlate of protection, and therefore measuring nAb responses is essential for monitoring vaccine efficacy. Fingerstick dried blood spots (DBS) are ideal for use in large-scale sero-surveillance because they are inexpensive, offer the option of self-collection and can be transported and stored at ambient temperatures. Such advantages also make DBS appealing to use in resource-limited settings and in potential future pandemics. In this study, nAb responses in sera, venous blood and fingerstick blood stored on filter paper were measured. Samples were collected from SARS-CoV-2 acutely infected individuals, SARS-CoV-2 convalescent individuals and SARS-CoV-2 vaccinated individuals. Good agreement was observed between the nAb responses measured in eluted DBS and paired sera. Stability of nAb responses was also observed in sera stored on filter paper at room temperature for 28 days. Overall, this study provides support for the use of filter paper as a viable sample collection method to study nAb responses. [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]

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]

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability–disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations. [ABSTRACT FROM AUTHOR]

During the COVID pandemic caused by the SARS-CoV-2 virus, studies have shown the efficiency of deactivating this virus via ultraviolet light. The damage mechanism is well understood: UV light disturbs the integrity of the RNA chain at those locations where specific nucleotide neighbors occur. In this contribution, we present a model to address certain gaps in the description of the interaction between UV photons and the RNA sequence for virus inactivation. We begin by exploiting the available information on the pathogen's morphology, physical, and genomic characteristics, enabling us to estimate the average number of UV photons required to photochemically damage the virus's RNA. To generalize our results, we have numerically generated random RNA sequences and checked that the distribution of pairs of nucleotides susceptible of damage for the SARS-CoV-2 is within the expected values for a random-generated RNA chain. After determining the average number of photons reaching the RNA for a preset level of fluence (or photon density), we applied the binomial probability distribution to evaluate the damage of nucleotide pairs in the RNA chain due to UV radiation. Our results describe this interaction in terms of the probability of damaging a single pair of nucleotides, and the number of available photons. The cumulative probability exhibits a steep sigmoidal shape, implying that a relatively small change in the number of affected pairs may trigger the inactivation of the virus. Our light-RNA interaction model quantitatively describes how the fraction of affected pairs of nucleotides in the RNA sequence depends on the probability of damaging a single pair and the number of photons impinging on it. A better understanding of the underlying inactivation mechanism would help in the design of optimum experiments and UV sanitization methods. Although this paper focuses on SARS-CoV-2, these results can be adapted for any other type of pathogen susceptible of UV damage. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic's early stages severely impacted global fisheries, particularly areas heavily reliant on imported food and tourism like the Galapagos Islands, Ecuador. To contain the spread of the virus, a full lockdown was implemented. However, the collapse of the tourism industry precipitated the worst economic crisis in the history of this multiple-use marine protected area. This paper examines the impact of the pandemic's early stages on consumption patterns and seafood security in the Galapagos from consumers' perspective, drawing on online surveys conducted during the lockdown. Our findings revealed pre-existing seafood insecurity across the archipelago, further exacerbated by the pandemic on the least-populated island. Nevertheless, the seafood system displayed moderated resilience to the pandemic's socioeconomic disruptions. A variety of adaptive responses were adopted by Galapagos residents to cope with the lockdown. Consumers modified their seafood consumption habits, while fishers adapted their harvesting and marketing strategies. Such adaptive responses were shaped by the unique socioeconomic characteristics of each inhabited island and the ability of seafood suppliers to shift from a tourism- and export-oriented to a resident- and domestic-oriented market. This transition has created novel opportunities to foster a systemic transformation of the Galapagos seafood system to enhance its resilience against future crises caused by new pandemics, climate change, or other natural and anthropogenic drivers of change. [ABSTRACT FROM AUTHOR]

A global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances. [ABSTRACT FROM AUTHOR]

The first two years of the COVID-19 pandemic and subsequent health mandates resulted in significant disruptions to daily life, creating a period of heightened psychosocial stress in myriad aspects. Understanding the impact of this period on pregnant individuals' bacteriomes is crucial as pregnancy is a period of heightened vulnerability to stress and its sequelae, anxiety and mood disorders, which have been demonstrated to alter gut microbiome composition. In a prospective cohort study (N = 12–26) conducted from February 2019 to August 2021, we examined psychometric responses and rectal microbiome swabs from pregnant individuals. Full-length 16 S rRNA sequencing followed by calculation of diversity metrics and relative abundance values were used to interrogate fecal microbiome community composition across pandemic groups. Distinct shifts in bacterial diversity and composition were observed during early to late pregnancy in the pandemic group, including lower relative abundance of pathogenic and lesser-known taxa. However, distribution of stress and depressive symptoms did not significantly differ from the pre-pandemic period while the correlation between stress and depressive symptoms dissipated during the pandemic. Our findings suggest that living through the COVID-19 pandemic altered the gut microbiome of pregnant individuals, independent of perceived stress. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a life-altering experience. It compelled individuals to alter their attitudes and habits and adopt measures to prevent the risks of infection. This research discusses factors that determine adherence to these measures, with the aim of enhancing compliance in future pandemics. A descriptive cross-sectional study was conducted using snowball non-random sampling through an online Qualtrics survey targeting Bahraini residents, aged 18 and older. The sample included 1,008 participants, and data were analyzed using SPSS, and presented in tables and graphs. The majority of the participants were Bahraini females with a bachelor's degree, between the ages of 20–29 (43.2%). Awareness of COVID-19 transmission was high, with 87.7%. The adherence rates for preventive measures were high: mask usage at 98.8%, hygiene practices at 93.7%, and social distancing at 92.2%. Older adults aged 50 + showed the highest consistency in compliance (p = 0.001). Individuals with chronic diseases were more likely to adhere to mask use (p < 0.001). Conversely, those experiencing negative emotions about social distancing were more likely to visit crowded places (p = 0.031), indicating a psychological barrier. Mask use exhibited the highest adherence at 98.8%, while overall compliance was notably high among educated and retired individuals. Public health campaigns should target younger demographics with education about crowded spaces and address emotional barriers to social distancing. Future strategies can be tailored to promote adherence among diverse populations, enhancing community resilience against pandemics. [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]

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]

Research on Guillain-Barré syndrome (GBS) as a neurological adverse effect of vaccines on a global scale is scarce, highlighting the need for further investigation to evaluate its long-term impact and associated risk factors comprehensively. Hence, this study aims to assess the global burden of vaccine-associated GBS and its associated vaccines. This study utilized data from VigiBase, the World Health Organization global database of adverse event reports of medicines and vaccines, encompassing the period from 1967 to 2023 (total reports, n = 131,255,418) to investigate vaccine-associated GBS. Reported odds ratios (ROR) and information components (IC) were analyzed to assess the association between 19 vaccines and the occurrence of vaccine-associated GBS over 170 countries. We identified 15,377 (8072 males [52.49%]) reports of vaccine-associated GBS among 22,616 reports of all drugs-cause GBS from 1978 to 2023. Cumulative reports of vaccine-associated GBS have been increasing steadily over time, with a notable surge observed since the commencement of COVID-19 vaccines administration in 2020. Most vaccines showed significant associations with GBS such as Ad5-vectored COVID-19 vaccines (ROR, 14.88; IC, 3.66), COVID-19 mRNA vaccines (ROR, 9.66; IC, 2.84), and inactivated whole-virus COVID-19 vaccines (ROR, 3,29; IC 1.69). Influenza vaccines showed the highest association (ROR, 77.91; IC 5.98). Regarding age-and sex-specific risks, the association remained similar regardless of sex, with an increased association observed with advancing age. The mean time to onset was 5.5 days. Amid the COVID-19 pandemic, the reports of GBS surged in response to widespread COVID-19 vaccination. Nonetheless, COVID-19 vaccines exhibited the lowest association compared to other vaccines. Vigilance for at least one-week post-vaccination is crucial, particularly for older adults. Further research is warranted to elucidate the underlying mechanisms linking vaccines and GBS. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, has raised significant interest in understanding potential cross-immunity mechanisms. Recent evidence suggests that T-cells associated with common cold coronaviruses (229E, NL63, OC43, HKU1) may provide some level of cross-immunity against SARS-CoV-2. It is also known that the prevalence of smokers among patients admitted to hospital for COVID-19 is lower than expected according to the corresponding country's smoking prevalence, which is known as smoker's paradox in COVID-19. No clear consensus to explain it has yet been reached. This phenomenon suggests a complex interaction between smoking and immune response. Nonetheless, very few works have studied the prevalence of smokers in those infected by common cold coronaviruses, and its relation to COVID-19 has not been investigated. We performed a systematic review and meta-analysis to study the prevalence of smokers among patients infected by common cold coronaviruses, and to compare them to the corresponding country's smoking prevalence. L'Abbé plots were used to visually assess the consistency of the observed effects across the different studies included in the meta-analysis. Additionally, significant differences were found in smoking prevalence among the various types of ccCoV, indicating the need for further research into the biological mechanisms driving these disparities. The results show that smoking prevalence is higher among those patients infected by these coronaviruses than in the general population (OR = 1.37, 95% CI: 0.81–2.33). A study was separately done for the four coronavirus types, and the prevalence of smokers was higher in three of the four than that corresponding to country, gender and study year: OC43 (OR = 1.93, 95% CI: 0.64–5.82); HKU1 (OR = 3.62, 95% CI: 1.21–10.85); NL63 (OR = 1.93, 95% CI: 0.64–5.82); 229E (OR = 0.97, 95% CI: 0.50–1.90). The heterogeneity of the studies was assessed using the Cochrane Chi-squared test, I-squared (I2), and Tau-squared (τ2). This detailed statistical analysis enhances the robustness of our findings and highlights the variations in smoking prevalence among different ccCoVs. Our data suggest that COVID-19 might be less prevalent among smokers due to greater cross-immunity from a larger number or more recent infections by common cold coronaviruses than the non-smoking population, which would explain smoker's paradox in COVID-19. IMPLICATIONS. The low prevalence of current smokers among SARS-CoV-2 patients is a finding recurrently repeated, even leading to postulate the "smoker's paradox" in COVID-19. This fact compelled us to study the prevalence of smokers among patients infected by common cold coronaviruses, and to compare them to the corresponding country's smoking prevalence. Our data could explain smoker's paradox in COVID-19 by a greater cross immunity due to a larger number, or more recent infections by common cold coronaviruses than the non-smoking population. This manuscript allow understand potential unrevealed mechanism for low prevalence of current smokers among SARS-CoV-2 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]

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]

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]

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]

Pandemics like COVID-19 have illuminated the significant disparities in the performance of national healthcare systems (NHCSs) during rapidly evolving crises. The challenge of comparing NHCS performance has been a difficult topic in the literature. To address this gap, our study introduces a bi-criteria longitudinal algorithm that merges fuzzy clustering with Data Envelopment Analysis (DEA). This new approach provides a comprehensive and dynamic assessment of NHCS performance and efficiency during the early phase of the pandemic. By categorizing each NHCS as an efficient performer, inefficient performer, efficient underperformer, or inefficient underperformer, our analysis vividly represents performance dynamics, clearly identifying the top and bottom performers within each cluster of countries. Our methodology offers valuable insights for performance evaluation and benchmarking, with significant implications for enhancing pandemic response strategies. The study’s findings are discussed from theoretical and practical perspectives, offering guidance for future health system assessments and policy-making. [ABSTRACT FROM AUTHOR]

Predicting the spread trends of a pandemic is crucial, but long-term prediction remains challenging due to complex relationships among disease spread stages and preventive policies. To address this issue, we propose a novel approach that utilizes data augmentation techniques, compartmental model features, and disease preventive policies. We also use a breakpoint detection method to divide the disease spread into distinct stages and weight these stages using a self-attention mechanism to account for variations in virus transmission capabilities. Finally, we introduce a long-term spread trend prediction model for infectious diseases based on a bi-directional gated recurrent unit network. To evaluate the effectiveness of our model, we conducted experiments using public datasets, focusing on the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85–4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread. [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]

Public health emergencies influence urban carbon emissions, yet an in-depth understanding of deviations between regional emissions under such emergencies and normal levels is lacking. Inspired by the concept of resilience, we introduce the concept of regional carbon resilience and propose four resilience indicators covering periods during and after emergencies. A synthetic difference-in-differences model is employed to compute these indicators, providing a more suitable approach than traditional methods assuming unchanged levels before and after emergencies. Using the COVID-19 pandemic in China as a case study, focusing on the power and industry sectors, we find that over 40% regions exhibit strong resilience (> 0.9). Average in-resilience (0.764 and 0.783) is higher than post-resilience (0.534 and 0.598) in both sectors, indicating lower resilience during than after emergencies. Significant differences in resilience performance exist across regions, with Hebei (0.93) and Hangzhou (0.92) as top performers, and Qinghai (0.29) and Guiyang (0.36) as the least resilient. Furthermore, a preliminary correlation analysis identifies 22 factors affecting carbon resilience; higher energy consumption, stronger industrial production, and a healthier regional economy positively contribute to resilience with coefficients over + 0.3, while pandemic severity negatively impacts resilience, with coefficients up to -0.58. These findings provide valuable references for policymaking to achieve carbon neutrality goals. [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]

The paper presents a novel statistical approach for analyzing the daily coronavirus case and fatality statistics. The survival discretization method was used to generate a two-parameter discrete distribution. The resulting distribution is referred to as the "Discrete Marshall–Olkin Length Biased Exponential (DMOLBE) distribution". Because of the varied forms of its probability mass and failure rate functions, the DMOLBE distribution is adaptable. We calculated the mean and variance, skewness, kurtosis, dispersion index, hazard and survival functions, and second failure rate function for the suggested distribution. The DI index demonstrates that the proposed model can represent both over-dispersed and under-dispersed data sets. We estimated the parameters of the DMOLBE distribution. The behavior of ML estimates is checked via a comprehensive simulation study. The behavior of Bayesian estimates is checked by generating 10,000 iterations of Markov chain Monte Carlo techniques, plotting the trace, and checking the proposed distribution. From simulation studies, it was observed that the bias and mean square error decreased with an increase in sample size. To show the importance and flexibility of DMOLBE distribution using two data sets about deaths due to coronavirus in China and Pakistan are analyzed. The DMOLBE distribution provides a better fit than some important discrete models namely the discrete Burr-XII, discrete Bilal, discrete Burr-Hatke, discrete Rayleigh distribution, and Poisson distributions. We conclude that the new proposed distribution works well in analyzing these data sets. The data sets used in the paper was collected from 2020 year. [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]

Internet of Things (IoT) technology can be used in many areas of everyday life. The objective of this paper is to obtain physiological functions in a non-invasive manner using commonly available IoT devices. The aim of the research is to point out the possibility of using physiological functions as an identifier of changes in students' level of arousal during the teaching process. The motivation of the work is to find a correlation between the change in heart rate, the student's level of arousal and the student's partial and final learning results. The research was focused on the collection of physiological data, namely heart rate and the evaluation of these data in the context of identification of arousal during individual teaching activities of the teaching process. The experiment was carried out during the COVID-19 pandemic via distance learning. During the teaching process, individual activities were recorded in time and HR was assigned to them. The benefit of the research is the proposed methodology of the system, which can identify changes in students' arousal in order to increase the efficiency of the teaching process. Based on the results of the designed system, they could also alert teachers who should be able to modify their teaching style in specific situations so that it is suitable for students and provides a basis for better teaching and understanding of educational materials. The presented methodology will be able to guarantee an increase in the success of the teaching process itself in terms of students' understanding of the teaching materials. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused significant disruptions to the daily lives of individuals worldwide, with many losing their lives to the virus. Vaccination has been identified as a crucial strategy to combat the spread of a disease, but with a limited supply of vaccines, targeted blocking is becoming increasingly necessary. One such approach is to block a select group of individuals in the community to control the spread of the disease in its early stages. Therefore, in this paper, a method is proposed for solving this problem, based on the similarity between this issue and the problem of identifying super-spreader nodes. The proposed method attempts to select the minimum set of network nodes that, when removed, no large component remains in the network. To this end, the network is partitioned into various communities, and a method for limiting the spread of the disease to communities is proposed by blocking connecting nodes. Four real networks and four synthetics networks created using the LFR algorithm were used to evaluate the control of the disease by the selected set of nodes using each method, and the results obtained indicate better performance of the proposed method compared to other methods. [ABSTRACT FROM AUTHOR]

SEIR (Susceptible–Exposed–Infected–Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available. [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]

Internet of Things (IoT) technology can be used in many areas of everyday life. The objective of this paper is to obtain physiological functions in a non-invasive manner using commonly available IoT devices. The aim of the research is to point out the possibility of using physiological functions as an identifier of changes in students' level of arousal during the teaching process. The motivation of the work is to find a correlation between the change in heart rate, the student's level of arousal and the student's partial and final learning results. The research was focused on the collection of physiological data, namely heart rate and the evaluation of these data in the context of identification of arousal during individual teaching activities of the teaching process. The experiment was carried out during the COVID-19 pandemic via distance learning. During the teaching process, individual activities were recorded in time and HR was assigned to them. The benefit of the research is the proposed methodology of the system, which can identify changes in students' arousal in order to increase the efficiency of the teaching process. Based on the results of the designed system, they could also alert teachers who should be able to modify their teaching style in specific situations so that it is suitable for students and provides a basis for better teaching and understanding of educational materials. The presented methodology will be able to guarantee an increase in the success of the teaching process itself in terms of students' understanding of the teaching materials. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a devastating impact on the health and wellbeing of the global population. This paper presents the results of a longitudinal transcultural study that was begun at the peak of the pandemic (in April, 2020). An online survey was used to collect data from English-, Spanish-, and Portuguese-speaking participants. The survey collected information about sociodemographics, lifestyle activities, COVID-19-related circumstances, and drug use (with an emphasis on hallucinogenic drugs), as well as involving psychometric questionnaires. Users of hallucinogenic drugs had higher psychological well-being and lower scores on psychopathology scales, both at baseline and during follow-ups. This difference was larger when users were distinguished by frequency of use, as regular users scored higher on psychological well-being and lower on psychopathology scales. Subjects with more psychological distress had lower scores for all scales of post-traumatic growth, but if they were regular hallucinogens users, they had higher scores for post-traumatic growth. When comparing the results between cultural contexts, heterogeneous results were obtained. There were more English-speaking regular users of hallucinogenic drugs. Further research should analyse the potential role of hallucinogens in large-scale catastrophes, with a special focus on post-traumatic growth. [ABSTRACT FROM AUTHOR]

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields. [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]

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

Since the pandemic outbreak of Covid-19 in December 2019, several lateral flow assay (LFA) devices were developed to enable the constant monitoring of regional and global infection processes. Additionally, innumerable lateral flow test devices are frequently used for determination of different clinical parameters, food safety, and environmental factors. Since common LFAs rely on non-biodegradable nitrocellulose membranes, we focused on their replacement by cellulose-composed, biodegradable papers. We report the development of cellulose paper-based lateral flow immunoassays using a carbohydrate-binding module-fused to detection antibodies. Studies regarding the protein binding capacity and potential protein wash-off effects on cellulose paper demonstrated a 2.7-fold protein binding capacity of CBM-fused antibody fragments compared to the sole antibody fragment. Furthermore, this strategy improved the spatial retention of CBM-fused detection antibodies to the test area, which resulted in an enhanced sensitivity and improved overall LFA-performance compared to the naked detection antibody. CBM-assisted antibodies were validated by implementation into two model lateral flow test devices (pregnancy detection and the detection of SARS-CoV-2 specific antibodies). The CBM-assisted pregnancy LFA demonstrated sensitive detection of human gonadotropin (hCG) in synthetic urine and the CBM-assisted Covid-19 antibody LFA was able to detect SARS-CoV-2 specific antibodies present in serum. Our findings pave the way to the more frequent use of cellulose-based papers instead of nitrocellulose in LFA devices and thus potentially improve the sustainability in the field of POC diagnostics. [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]

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]

This study aimed to investigate the relationship between pre-pandemic objective and subjective cognitive functioning and sustained Internet use during the pandemic among older adults in Switzerland. Data from 1299 respondents of the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2019/2020 and a supplementary technology use questionnaire during the pandemic in 2021 were used. Cognitive functioning was assessed in 2019/2020 through objective measures (delayed and immediate memory, verbal fluency) and self-rated memory. Sustained Internet use was defined as having used the Internet at least once in the past seven days in 2019/2020 and reporting daily or weekly use in 2021. We found that 73.1% of respondents consistently used Internet between 2019/2020 and 2021. Using multivariable probit regression models controlling for sociodemographic and health variables, we found that higher global cognition z-scores, especially in immediate and delayed memory, were associated with a higher likelihood of sustained Internet use. Additionally, respondents with good, very good, or excellent self-rated memory were more likely to sustain their Internet use. These findings highlight the potential critical role of cognitive health in shaping older adults' digital engagement, suggesting that cognitive assessments and training should be further considered in digital literacy initiatives for this population. [ABSTRACT FROM AUTHOR]

In late 2020, the focus of the global effort against the COVID-19 pandemic centered around the development of a vaccine, when reports of a mutated SARS-CoV-2 virus variant in a population of 17 million farmed mink came from Denmark, threatening to jeopardize this effort. Spillover infections of the new variant between mink and humans were feared to threaten the efficacy of upcoming vaccines. In this study the ensuing short-lived yet stringent lockdowns imposed in 7 of the countries 98 municipalities are analysed for their effectiveness to reduce SARS-CoV-2 infections. Synthetic counterfactuals are created for each of these municipalities using a weighted average combination of the remaining municipalities not targeted by the stringent measures. This allows for a clear overview regarding the development of test-positivity rates, citizen mobility behaviours and lastly daily infection numbers in response to the restrictions. The findings show that these targeted, short-term lockdowns significantly curtailed further infections, demonstrating a marked decrease, first in citizens mobility and then in daily cases when compared to their synthetic counterfactuals. Overall, the estimates indicate average reductions to infection numbers to be around 31%. This study underscores the potential of strict, yet severe lockdowns in breaking ongoing infection dynamics, by utilising a rare quasi-experimental design case that avoids bias introduced through treatment selection. [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]

Antibiotics, improper food, and stress have created a dysbiotic state in the gut and almost 81% of the world's population has been affected due to the pandemic of COVID-19 and the prevalence of dengue virus in the past few years. The main intent of this study is to synthesize nanosynbiotics as nu traceuticals by combining probiotics, and prebiotics with nanoformulation. The effectiveness of the nanosynbiotics was evaluated using a variety of Nutra-pharmacogenetic assays leading to an AI-integrated formulation profiling was assessed by using machine learning methods. Consequently, Acetobacter oryzoeni as a probiotic and inulin as a prebiotic has been chosen and iron-mediated nanoformulation of symbiotic is achieved. Nanosynbiotics possessed 89.4, 96.7, 93.57, 83.53, 88.53% potential powers of Nutra-pharmacogenetic assays. Artificial intelligent solid dispersion formulation of nanosynbiotics has high dissolution, absorption, distribution, and synergism, in addition, they are non-tox, non-allergen and have a docking score of − 10.83 kcal/mol, implying the best interaction with Pregnane X receptor involved in dysbiosis. The potential of nanosynbiotics to revolutionize treatment strategies through precise targeting and modulation of the gut microbiome for improved health outcomes and disease management is promising. Their transformational influence is projected to be powered by integration with modern technology and customized formulas. Further in-vivo studies are required for the validation of nanosynbiotics as nutraceuticals. [ABSTRACT FROM AUTHOR]