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In this paper, we tackle the problem of forecasting future pandemics by training models with a COVID-19 time series. We tested this approach by producing one model and using it to forecast a non-trained time series; however, we limited this paper to the eight states with the highest population density in Mexico. We propose a generalized pandemic forecasting framework that transforms the time series into a dataset via three different transformations using random forest and backward transformations. Additionally, we tested the impact of the horizon and dataset window sizes for the training phase. A Wilcoxon test showed that the best transformation technique statistically outperformed the other two transformations with 100% certainty. The best transformation included the accumulated efforts of the other two plus a normalization that helped rescale the non-trained time series, improving the sMAPE from the value of 25.48 attained for the second-best transformation to 13.53. The figures in the experimentation section show promising results regarding the possibility of forecasting the early stages of future pandemics with trained data from the COVID-19 time series. [ABSTRACT FROM AUTHOR]

Polypharmacy, the use of multiple and potentially inappropriate medications, is an increasing problem among older adults. The global polypharmacy prevalence is 34.6% in patients with COVID-19, and polypharmacy in COVID-19 increases with age. The present paper proposes that polypharmacy in older adults with COVID-19 and other comorbid conditions is linked to the medication outcome reporting bias of randomized controlled trials. Outcome reporting bias can occur when treatment efficacy is reported as relative risk reductions, which overestimates medication benefits and exaggerates disease/illness risk reductions compared to unreported absolute risk reductions. The comorbidities common in patients with COVID-19 include high blood pressure, cardiovascular disease, dementia or cerebrovascular disease, and diabetes. Accordingly, the present paper reassesses the relative and absolute risk reductions in clinical trials from a small convenience sample of antihypertension, statin, anticoagulant, and antihyperglycemic medications. Examples demonstrate a wide gap between reported relative risk reductions and unreported absolute risk reductions in medication clinical trials. This paper concludes that medication clinical trial outcome reporting bias is an important upstream factor that contributes to biased medication benefits and poor clinical decision making, leading to polypharmacy in older adults with COVID-19 and other comorbid conditions. Public health campaigns are urgently needed to educate the public about the link between polypharmacy and medication outcome reporting bias. [ABSTRACT FROM AUTHOR]

Competing risks survival analysis is used to answer questions about the time to occurrence of events with the extension of multiple causes of failure. Studies that investigate how clinical features and risk factors of COVID-19 are associated with the survival of patients in the presence of competing risks (CRs) are limited. The main objective of this paper is, under a CRs setting, to estimate the Cumulative Incidence Function (CIF) of COVID-19 death, the CIF of other-causes death, and the probability of being cured in subjects with COVID-19, who have been under observation from the date of symptoms to the date of death or exit from the study because they are cured. In particular, we compared the non-parametric estimator of the CIF based on the naive technique of Kaplan–Meier (K–M) with the Aalen–Johansen estimator based on the cause-specific approach. Moreover, we compared two of the most popular regression approaches for CRs data: the cause-specific hazard (CSH) and the sub-distribution hazard (SDH) approaches. A clear overestimation of the CIF function over time was observed under the K–M estimation technique. Moreover, exposure to asthma, diabetes, obesity, older age, male sex, black and indigenous races, absence of flu vaccine, admission to the ICU, and the presence of other risk factors, such as immunosuppression and chronic kidney, neurological, liver, and lung diseases, significantly increased the probability of COVID-19 death. The highest hazard ratio of 2.03 was observed for subjects with an age greater than 70 years compared with subjects aged 50–60 years. The SDH approach showed slightly higher survival probabilities compared with the CSH approach. An important foundation for producing precise individualized predictions was provided by the competing risks regression models discussed in this paper. This foundation allowed us, in general, to more realistically model complex data, such as the COVID-19 data, and can be used, for instance, by many modern statistical learning and personalized medicine techniques to obtain more accurate conclusions. [ABSTRACT FROM AUTHOR]

The use of corpus assessment approaches to determine and rank keywords for corpus data is critical due to the issues of information retrieval (IR) in Natural Language Processing (NLP), such as when encountering COVID-19, as it can determine whether people can rapidly obtain knowledge of the disease. The algorithms used for corpus assessment have to consider multiple parameters and integrate individuals' subjective evaluation information simultaneously to meet real-world needs. However, traditional keyword-list-generating approaches are based on only one parameter (i.e., the keyness value) to determine and rank keywords, which is insufficient. To improve the evaluation benefit of the traditional keyword-list-generating approach, this paper proposed an extended analytic hierarchy process (AHP)-based corpus assessment approach to, firstly, refine the corpus data and then use the AHP method to compute the relative weights of three parameters (keyness, frequency, and range). To verify the proposed approach, this paper adopted 53 COVID-19-related research environmental science research articles from the Web of Science (WOS) as an empirical example. After comparing with the traditional keyword-list-generating approach and the equal weights (EW) method, the significant contributions are: (1) using the machine-based technique to remove function and meaningless words for optimizing the corpus data; (2) being able to consider multiple parameters simultaneously; and (3) being able to integrate the experts' evaluation results to determine the relative weights of the parameters. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic drew public attention to the essential work and vulnerability of low-income Latina immigrants. Less recognized were the ways immigrant community organizations mobilized under exceptional conditions to provide immediate support to their communities while continuing to work toward durable systematic change. This paper analyzes the approach of Mujeres Unidas y Activas (MUA) in the San Francisco Bay Area. Over three decades, MUA developed an organizing model that builds transformative relationships among peers and provides direct services and leadership development for civic engagement. MUA has a long history of research collaborations and self-study aligned with critical community-engaged research methods and values. In 2019, MUA formed a research team of its leaders and academics to analyze the impact of their model. Since data collection occurred between March 2020 and December 2022, the research also documented the organization's response to COVID-19. This paper argues that specific organizational values and practices of liderazgo, apoyo, and confianza (leadership, support, and trust) proved to be particularly powerful resources for sustaining individuals and community work through the pandemic, enabling women who have experienced multiple forms of structural violence to perceive themselves as capable of healing themselves and their communities while working to address root causes of trauma and inequity. [ABSTRACT FROM AUTHOR]

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability ( E k ) as the criteria to select the most promising variables for clustering. E k is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making. [ABSTRACT FROM AUTHOR]

The paper examines how Hindu nationalist social service organizations, specifically the Deseeya Seva Bharathi (DSB), reconfigured the religious conception of 'Seva' to advance the project of constructing a Hindu social identity during the COVID-19 pandemic in the state of Kerala. The southern Indian state of Kerala has remained an exception in the story of the rise of the Hindu nationalist movement in contemporary India, which has repeatedly failed to make any considerable political inroads in the state. However, the disastrous economic consequences and livelihood challenges during the COVID-19 pandemic in the state, which was heavily dependent on foreign remittance and service industries, have opened up new spaces of engagement for Hindu nationalists. Drawing on the fieldwork conducted in central Kerala during the pandemic, this paper will elaborate on how the DSB used the crisis moment of the pandemic to reach out to economically and socially disadvantaged communities using the language of 'Seva' to build a Hindu social identity, which imbues the influence of majoritarian Hindu nationalist politics. The paper argues that the DSB's articulation of 'Seva' as a distinct and superior form of social service that is 'self-less', 'non-instrumental' and 'non-reciprocal' is significant in understanding the growing appeal of Hindu nationalist social service in the contested political sphere of Kerala, which is marked by competing social provisions by the state as well as other secular and religious groups. The paper notes that the reconfiguration of 'Seva' as a continuous religious concept enables Hindu nationalists to attain greater acceptance and legitimacy that even the secular state welfare could not achieve, while also concealing the inherent instrumental nature of its social service towards the construction of a Hindu social identity in the region. [ABSTRACT FROM AUTHOR]

Infection with SARS-CoV-2 can lead to various long-term consequences, including those of an ophthalmic nature. This paper reviews the results of optical coherence tomography angiography (OCTA) performed among COVID-19 patients. The review included papers evaluating short- and long-term outcomes following the SARS-CoV-2 infection. Some differentiated the obtained retinal and choroidal vascularization parameters according to gender. Following COVID-19, patients reveal changes in retinal and choroidal vascular parameters based on OCTA, such as reduced vascular density and an increased foveal avascular zone, which can persist for several months. Routine ophthalmic follow-up with OCTA should be considered in patients after SARS-CoV-2 infection to assess the effects of inflammation and systemic hypoxia in COVID-19. Further research is needed to understand whether infection with particular viral variants/subvariants may vary in the risk of effects on retinal and choroidal vascularization and whether and to what extent these risks may also differ in relation to reinfected and vaccinated individuals. [ABSTRACT FROM AUTHOR]

The transmission rate is an important indicator for characterizing a virus and estimating the risk of its outbreak in a certain area, but it is hard to measure. COVID-19, for instance, has greatly affected the world for more than 3 years since early 2020, but scholars have not yet found an effective method to obtain its timely transmission rate due to the fact that the value of COVID-19 transmission rate is not constant but dynamic, always changing over time and places. Therefore, in order to estimate the timely dynamic transmission rate of COVID-19, we performed the following: first, we utilized a rolling time series to construct a time-varying transmission rate model and, based on the model, managed to obtain the dynamic value of COVID-19 transmission rate in mainland China; second, to verify the result, we used the obtained COVID-19 transmission rate as the explanatory variable to conduct empirical research on the impact of the COVID-19 pandemic on China's stock markets. Eventually, the result revealed that the COVID-19 transmission rate had a significant negative impact on China's stock markets, which, to some extent, confirms the validity of the used measurement method in this paper. Notably, the model constructed in this paper, combined with local conditions, can not only be used to estimate the COVID-19 transmission rate in mainland China but also in other affected countries or regions and would be applicable to calculate the transmission rate of other pathogens, not limited to COVID-19, which coincidently fills the gaps in the research. Furthermore, the research based on this model might play a part in regulating anti-pandemic governmental policies and could also help investors and stakeholders to make decisions in a pandemic setting. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice. [ABSTRACT FROM AUTHOR]

In this paper, using data from Romania, we analysed the changes in electricity consumption generated during the COVID-19 crisis, and the measures taken against the spread of the coronavirus to limit the effects of the pandemic. Using a seasonal autoregressive econometric model, we found that, beyond seasonal (weekly, monthly, quarterly, yearly) effects, the average daily electricity real consumption in Romania, during the state of the emergency period (16 March 16 to 14 May 2020) decreased by −194.8 MW (about −2.9%), compared to the historical data (2006–March 2022), and this decrease is not due to the action of some random factors, and it is not a manifestation of domain-specific seasonality. The literature discusses the hypothesis that during the pandemic time, the profile of daily electricity consumption on weekdays was close to the typical Sunday profile. We tested a similar hypothesis for Romania. As a methodology, we tried to go beyond the simple interpretation of statistics and graphics (as found in most papers) and we calculated some measures of distances (the Mahalanobis distance, Manhattan distance) and similarity (coefficient of correlation, cosines coefficient) between the vectors of daily electricity real consumptions, by hourly intervals. As the time interval, we have analysed, for Romania, the electricity real consumption over the period January 2006–March 2022, by day of the week and within the day, by hourly intervals (5911 observations). We found (not very strong) evidence supporting a hypothesis that, in the pandemic crisis, the profile of electricity consumption approaches the weekend pattern only for the state of the emergency period, and we could not find the same evidence for the state of the alert period (June 2020–March 2022). The strongest closeness is to the hourly consumption pattern of Saturday. That is, for Romania, in terms of electricity consumption, "under lockdown, every day is a Sunday" (Staffell) it is rather "under lockdown, every day is (almost) a Saturday"! During the state of the alert period, consumption returned to the pre-crisis profile. Since certain behaviours generated by the pandemic have been maintained in the medium and long term (distance learning, working from home, online sales, etc.), such studies can have policy implications, especially for setting energy policy measures (e.g., in balancing load peaks). [ABSTRACT FROM AUTHOR]

This article examines the controversy over the mode of distribution of Holy Communion that surfaced during the COVID-19 pandemic, with a focus on debates that took place in the Greek Orthodox community. After describing and evaluating the role of secular and religious experts in the context of the pandemic, the paper analyzes three main perspectives on the issue of the Eucharist: (1) the secularist-rationalist viewpoint; (2) the religious–traditionalist outlook; and (3) the "Third Way" perspective. The paper argues that the Church's Holy Communion controversy is indicative of a deeper struggle between religious and secular thinkers and among various voices in the Greek Orthodox Church concerning the latter's place in, and influence over, the modern secular socio-political order. [ABSTRACT FROM AUTHOR]

In this paper, we present a systematic review and meta-analysis that aims to evaluate the reliability of coronavirus disease diagnostic tests in 2019 (COVID-19). This article seeks to describe the scientific discoveries made because of diagnostic tests conducted in recent years during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Between 2020 and 2021, searches for published papers on the COVID-19 diagnostic were made in the PubMed database. Ninety-nine scientific articles that satisfied the requirements were analyzed and included in the meta-analysis, and the specificity and sensitivity of the diagnostic accuracy were assessed. When compared to serological tests such as the enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay (CLIA), lateral flow immunoassay (LFIA), and chemiluminescent microparticle immunoassay (CMIA), molecular tests such as reverse transcription polymerase chain reaction (RT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) performed better in terms of sensitivity and specificity. Additionally, the area under the curve restricted to the false-positive rates (AUCFPR) of 0.984 obtained by the antiviral neutralization bioassay (ANB) diagnostic test revealed significant potential for the identification of COVID-19. It has been established that the various diagnostic tests have been effectively adapted for the detection of SARS-CoV-2; nevertheless, their performance still must be enhanced to contain potential COVID-19 outbreaks, which will also help contain potential infectious agent outbreaks in the future. [ABSTRACT FROM AUTHOR]

One of the major public health measures to manage and contain the outbreak of the COVID-19 pandemic was to engage in systematic contact tracing, which required gastronomy, community and sporting venues to keep patron registers. Stand-alone and web-based applications, developed by a range of private IT providers, soon replaced pen-and-paper lists. With the introduction of a uniform, state-wide, mandatory data collection system, these private applications became obsolete. Although only active for four months, these applications paved the way for the public acceptance of state-administered collection systems that allowed for an unprecedented, centralized tracking system of the movements of the entire population. This paper discusses the cultural significance of these applications as a game changer in the debate on civil liberties, and addresses the question of how the materiality, or lack thereof, of this digital heritage affects the management of ephemeral smartphone applications, and its preservation for future generations. [ABSTRACT FROM AUTHOR]

This paper deals with COVID-19-hit Kenyan slums and the numerous interventions civil society organizations implemented during the first waves of the pandemic since it was initially detected in March 2020. As part of a comprehensive project using mixed methodology, including desk research and key informant interviews, community-based organizations and non-governmental organizations, together with other stakeholders of their collaborative networks, were investigated regarding the roles that they played, the projects they carried out, and the interventions they were involved in in the mitigation of the negative impacts of COVID-19. This paper investigates how COVID-19 actually hit Kenyan slums and how it affected civil society organizations during the pandemic. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer. [ABSTRACT FROM AUTHOR]

The touristic use of sacred sites is a widespread practice in Hungary. Throughout the past centuries, Christianity has dominated the history of the Carpathian Basin. The Hungarian State's strong affiliation with the Roman Catholic Church dates back to its foundation over a 1000 years ago. Due to its location on the shore of Lake Balaton and outstanding natural environment, the Benedictine Tihany Abbey is a major touristic destination and a popular place of pilgrimage in Hungary. The objective of the present paper is to examine how touristic activities contribute to the livelihood of a Hungarian monastic community and local economic development in the 21st century. To answer the research questions, the study primarily relied on literature and documentary analysis, in-depth interviews, and the construction and processing of a database. The research revealed that in addition to the classic elements (e.g., guided tours in the abbey, museum exhibitions, concerts, etc.), the program offer developed by the monastic community of Tihany is capable of addressing new target audiences: the rapidly rising number of tourists from the Far East (mainly Japan, China, South Korea, and Russia). Despite remaining considerably below visitor numbers recorded by the abbey in the mid-1990s, a significant increase in visitor numbers was observed in 2018–2019. The economic activities of the Benedictines of Tihany comprise a number of different areas beyond tourism, e.g., agricultural production and candle making, both of which are integrated into their tourism offer and feature among touristic programs. As a major employer, the abbey exerts a positive impact on the population retention capacity of the settlement by offering jobs to local residents, and it also contributes to population growth by attracting a large number of highly skilled professionals who choose to settle down in the region. The paper shows the evidence of the pandemic on pilgrimage and religious tourism in Tihany. [ABSTRACT FROM AUTHOR]

The lack of access to basic services played a big part among the key effects of COVID-19 on migrants and refugees. This paper examines the governance dynamics behind public services for migrants and refugees to understand how COVID-19 has impacted them and what accounts for different levels of adaptive capacity. It employs a mixed methods approach, using egocentric network analysis and qualitative interviews to compare the service ecosystems in four European cities from 2020 to 2022 (Birmingham, Larissa, Malaga, and Palermo). The paper explores the impact of two conditions on the service ecosystems' ability to adapt to the pandemic: the structure of governance and the presence of dynamic capabilities. We argue that the ability of local governments to manage pandemic challenges is highly dependent on the formal distribution of comprehensive competences across various levels (the structure of governance), and the quality of network cooperation between different administrations and civil society (dynamic capabilities). Our analysis reveals that while both conditions are critical for the level of adaptive capacity in public services' provision, the structure of governance is more likely to act as a constraint or trigger for coping strategies. [ABSTRACT FROM AUTHOR]

The aim of this concept paper is the description of a new tool to support institutions in the implementation of targeted countermeasures, based on quantitative and multi-scale elements, for the fight and prevention of emergencies, such as the current COVID-19 pandemic. The tool is a cloud-based centralized system; a multi-user platform that relies on artificial intelligence (AI) algorithms for the processing of heterogeneous data, which can produce as an output the level of risk. The model includes a specific neural network which is first trained to learn the correlations between selected inputs, related to the case of interest: environmental variables (chemical–physical, such as meteorological), human activity (such as traffic and crowding), level of pollution (in particular the concentration of particulate matter) and epidemiological variables related to the evolution of the contagion. The tool realized in the first phase of the project will serve later both as a decision support system (DSS) with predictive capacity, when fed by the actual measured data, and as a simulation bench performing the tuning of certain input values, to identify which of them led to a decrease in the degree of risk. In this way, we aimed to design different scenarios to compare different restrictive strategies and the actual expected benefits, to adopt measures sized to the actual needs, adapted to the specific areas of analysis and useful for safeguarding human health; and we compared the economic and social impacts of the choices. Although ours is a concept paper, some preliminary analyses have been shown, and two different case studies are presented, whose results have highlighted a correlation between NO 2 , mobility and COVID-19 data. However, given the complexity of the virus diffusion mechanism, linked to air pollutants but also to many other factors, these preliminary studies confirmed the need, on the one hand, to carry out more in-depth analyses, and on the other, to use AI algorithms to capture the hidden relationships among the huge amounts of data to process. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is still ongoing, even if the emergency is over, and we now have enough data to analyze the outbreak over a long timeline. There is evidence that the outbreak alternates periods of high and low infections. Retrospectively, this can help in understanding the nature of an appropriate mathematical model for this dramatic infection. The periodic behavior may be the consequence of time-dependent coefficients related to seasonal effects and specific political actions, or an intrinsic feature of the model. The present paper relies on the assumption that the periodic spikes are an intrinsic feature of the disease, and, as such, it should be properly reflected in the mathematical model. Based on the concept of waning immunity proposed for other pathologies, we introduce a new model with (i) a compartment for weakly immune people subject to immunity booster, represented by a non-linear term; (ii) discrimination between individuals infected/vaccinated for the first time, and individuals already infected/vaccinated, undergoing to new infections/doses. We analyze some preliminary properties of our model, called SIRW2, and provide a proof-of-concept that it is capable of reproducing qualitatively the long-term oscillatory behavior of COVID-19 infection. [ABSTRACT FROM AUTHOR]

Although COVID-19 transmission has been reduced by the advent of vaccinations and a variety of rapid monitoring techniques, the SARS-CoV-2 virus itself has shown a remarkable ability to mutate and persist. With this long track record of immune escape, researchers are still exploring prophylactic treatments to curtail future SARS-CoV-2 variants. Specifically, much focus has been placed on the antiviral lectin Griffithsin in preventing spike protein-mediated infection via the hACE2 receptor (direct infection). However, an oft-overlooked aspect of SARS-CoV-2 infection is viral capture by attachment receptors such as DC-SIGN, which is thought to facilitate the initial stages of COVID-19 infection in the lung tissue (called trans-infection). In addition, while immune escape is dictated by mutations in the spike protein, coronaviral virions also incorporate M, N, and E structural proteins within the particle. In this paper, we explored how several structural facets of both the SARS-CoV-2 virion and the antiviral lectin Griffithsin can affect and attenuate the infectivity of SARS-CoV-2 pseudovirus. We found that Griffithsin was a better inhibitor of hACE2-mediated direct infection when the coronaviral M protein is present compared to when it is absent (possibly providing an explanation regarding why Griffithsin shows better inhibition against authentic SARS-CoV-2 as opposed to pseudotyped viruses, which generally do not contain M) and that Griffithsin was not an effective inhibitor of DC-SIGN-mediated trans-infection. Furthermore, we found that DC-SIGN appeared to mediate trans-infection exclusively via binding to the SARS-CoV-2 spike protein, with no significant effect observed when other viral proteins (M, N, and/or E) were present. These results provide etiological data that may help to direct the development of novel antiviral treatments, either by leveraging Griffithsin binding to the M protein as a novel strategy to prevent SARS-CoV-2 infection or by narrowing efforts to inhibit trans-infection to focus on DC-SIGN binding to SARS-CoV-2 spike protein. [ABSTRACT FROM AUTHOR]

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience. [ABSTRACT FROM AUTHOR]

The pathophysiology of the severe course of COVID-19 is multifactorial and not entirely elucidated. However, it is well known that the hyperinflammatory response and cytokine storm are paramount events leading to further complications. In this paper, we investigated the vascular response in the pathophysiology of severe COVID-19 and aimed to identify novel biomarkers predictive of ICU admission. The study group consisted of 210 patients diagnosed with COVID-19 (age range: 18–93; mean ± SD: 57.78 ± 14.16), while the control group consisted of 80 healthy individuals. We assessed the plasma concentrations of various vascular factors using the Luminex technique. Then, we isolated RNA from blood mononuclear cells and performed a bioinformatics analysis investigating various processes related to vascular response, inflammation and angiogenesis. Our results confirmed that severe COVID-19 is associated with vWF/ADAMTS 13 imbalance. High plasma concentrations of VEGFR and low DPP-IV may be potential predictors of ICU admission. SARS-CoV-2 infection impairs angiogenesis, hinders the generation of nitric oxide, and thus impedes vasodilation. The hypercoagulable state develops mainly in the early stages of the disease, which may contribute to the well-established complications of COVID-19. [ABSTRACT FROM AUTHOR]

In the United States, there has been a long history of environmental injustice that disproportionately affects racial and ethnic minorities and low-income communities due to racially targeted policies and widespread discrimination. Environmental racism can be revealed in housing discrimination that perpetuates inequities in exposure to environmental pollutants. Biased credit and mortgaging practices such as redlining have led to housing segregation of racial and ethnic minorities in the USA, permitting policymakers to diminish and disinvest in these communities. The COVID-19 pandemic has amplified housing instability for families of color, including Black and Hispanic/Latinx communities, putting them at increased risk for COVID-19 exposure. There is a need to investigate how environmental injustice intensifies the COVID-19 pandemic, illuminates racial and ethnic inequities in exposure to environmental contaminants, and fuels disparities in COVID-19 outcomes. The aims of this paper are to analyze and discuss environmental injustice and racial and ethnic disparities related to COVID-19 and housing. We also propose recommendations to address this pervasive issue. [ABSTRACT FROM AUTHOR]

Bearing in mind the socio-economic consequences of COVID-19 on airports and passengers, this study considers cleaning and hygiene a top priority in airports and airlines' operating processes. Both airport operators and passengers should be aware that the COVID-19 pathogen has not yet gone away and, thus, continues to spread despite mass vaccination. In this context, this paper's objective is to show the importance of cleaning and hygiene in the air transport industry to encourage the passengers' confidence in making decisions to travel on commercial airlines and help airport and airline operators make better decisions in their efforts to improve the cleaning and hygiene protocols. Passengers' awareness of hygiene and cleanliness has increased, but unfortunately, airports and airlines have reduced cleaning and hygiene activities to save costs. Although there is much to do to help the aviation sector recover, we also see plenty of reasons to be optimistic about the air transport industry's future, as long as airport and airline operators can guarantee an adequate quality of service with regard to cleaning and hygiene in order to ensure passengers' safety and well-being. Airports and airlines cannot lower their guard because the SARS, H1N1, MERS, and COVID-19 viruses remain active in society. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, regions were affected by a combination of economic crises: weak demand and constrained supply. Several studies have sought to analyse the heterogeneous effects of supply and demand shocks on the labour market, economic growth, and the environment. This study has a different focus, estimating both direct and indirect effects of demand and supply shocks adopted during the pandemic in Brazil and Chile. Afterwards, the paper compares the degree of regional absorption (leakage) of income resulting from each of these shocks, applying an interregional input–output model for each country. The results of this study show that income absorption by the poorest regions is relatively greater in the case of a supply shock. It can be said, therefore, that this type of shock improves the retention of income generated in the poorest regions, favouring the development of these localities and the reduction in regional inequalities. The main reason for this result is that supply policies have restricted essential sectors to a lesser extent, and these sectors are generally less concentrated in large urban centres in both Brazil and Chile. In other words, much of the interregional leakage is driven by the demand for non-essential products, mainly in the richest urban economy centres. Finally, the geographical dimension of regional inequalities leads to the economic benefit of prosperous areas in the country when shocks occur in vulnerable regions, highlighting the centre–periphery pattern in both countries. [ABSTRACT FROM AUTHOR]

Several animal species have been found to be susceptible to SARS-CoV-2 infection. The occurrence of infection in dogs and cats living in close contact with owners deserves particular attention from public health authorities in a One Health approach. In this study, we conducted serological screening to identify SARS-CoV-2 exposure in the sera from dogs and cats in three regions of southern Italy sampled during the years 2021 and 2022. We collected 100 serum samples in 2021 (89 from dogs and 11 from cats) and 640 in 2022 (577 from dogs and 63 from cats). Overall, the ELISA positivity rate was found to be 2.7% (20/740), with higher seroprevalence in dogs. Serum neutralization tests confirmed positivity only in two samples collected from dogs, and the assays, performed with serologically distinct SARS-CoV-2 variants, showed variant-specific positivity. This paper shows that monitoring SARS-CoV-2 exposure in animals might be affected by the viral antigenic evolution, which requires continuous updates to the serological tests used. Serological surveys are useful in understanding the true extent of exposure occurring in specific animal populations, not suffering the same limitations as molecular tests, and could help in identifying the infecting virus if tests able to characterize the immune response are used. The use of variant-specific validated serological methods should always be considered in serosurvey studies in order to determine the real impact of emerging variants on animal populations and its implications for veterinary and human health, as well as to identify potential reservoirs of the virus and its evolutionary changes. [ABSTRACT FROM AUTHOR]

Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long COVID with acute infection at the population level, our modeling framework emphasizes the interplay between COVID-19 transmission, vaccination, and long COVID dynamics. We conducted a detailed mathematical analysis of the model. We also validated the model using numerical simulation with real data from the US state of Tennessee and the UK. [ABSTRACT FROM AUTHOR]

We highlighted in this current paper similar prolonged respiratory presentation with COVID-19 pneumonia in four severely immunocompromised patients currently being treated with anti-CD20 monoclonal antibodies (mAbs), such as ocrelizumab and rituximab, for multiple sclerosis or rheumatoid polyarthritis. Real-time reverse transcription-polymerase chain reaction on a nasopharyngeal swab specimen was negative in all patients. SARS-CoV-2 infection was confirmed from bronchoalveolar lavage fluid. A high titer of post-vaccine COVID-19 convalescent plasma was administered with complete recovery in all patients. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has become a serious challenge for humanity almost everywhere globally. Despite active vaccination around the world, the incidence proportion in different countries varies significantly as of May 2022. The reason may be a combination of demographic, immunological, and epidemiological factors. The purpose of this study was to analyze possible relationships between COVID-19 incidence proportion in the population and the types of SARS-CoV-2 vaccines used in different countries globally, taking into account demographic and epidemiological factors. Materials and methods: An initial database was created of demographic and immunoepidemiological information about the COVID-19 situation in 104 countries collected from published official sources and repository data. The baseline included, for each country, population size and density; SARS-CoV-2 testing coverage; vaccination coverage; incidence proportion; and a list of vaccines that were used, including their relative share among all vaccinations. Subsequently, the initial data set was stratified by population and vaccination coverage. The final data set was subjected to statistical processing both in general and taking into account population testing coverage. Results: After formation of the final data set (including 53 countries), it turned out that reported COVID-19 case numbers correlated most strongly with testing coverage and the proportions of vaccine types used, specifically, mRNA (V1); vector (V2); peptide/protein (V3); and whole-virion/inactivated (V4). Due to the fact that an inverse correlation was found between 'reported COVID-19 case numbers' with V2, V3, and V4, these three vaccine types were also combined into one analytic group, 'non-mRNA group' vaccines (Vnmg). When the relationship between vaccine type and incidence proportion was examined, minimum incidence proportion was noted at V1:Vnmg ratios (%:%) from 0:100 to 30:70. Maximum incidence proportion was seen with V1:Vnmg from 80:20 to 100:0. On the other hand, we have shown that the number of reported COVID-19 cases in different countries largely depends on testing coverage. To offset this factor, countries with low and extremely high levels of testing were excluded from the data set; it was then confirmed that the largest number of reported COVID-19 cases occurred in countries with a dominance of V1 vaccines. The fewest reported cases were seen in countries with a dominance of Vnmg vaccines. Conclusion: In this paper, we have shown for the first time that the level of reported COVID-19 incidence proportion depends not only on SARS-CoV-2 testing and vaccination coverage, which is quite logical, but probably also on the vaccine types used. With the same vaccination level and testing coverage, those countries that predominantly use vector and whole-virion vaccines feature incidence proportion that is significantly lower than countries that predominantly use mRNA vaccines. [ABSTRACT FROM AUTHOR]

In response to the low representation of Latinx adults in STEM occupations, this community-based participatory action research study aims to increase the number of middle school youths developing STEM career identities and entering high school with the intention to pursue STEM careers. The students were provided with summer and after-school activities focusing on network science and career development curricula. Using a quasi-experimental pretest–posttest design and career narratives, this study examined the changes in STEM and career self-efficacy, as well as career identity. The results show improvements in self-efficacy, an increased number of youths with intentions of pursuing future STEM career opportunities, and deeper reflections on their talents and skills after program participation. This paper also describes the program development and implementation in detail, as well as the adaptations that resulted from COVID-19, for scholars and educators designing similar programs. This study provides promising evidence for the quality of STEM and career development lessons in supporting the emergence of a STEM career identity and self-efficacy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic substantially impacted the mental health of the general population and particularly vulnerable individuals and groups. A wealth of research allows for estimating this impact and identifying relevant factors contributing to or mitigating it. The current paper presents and synthesizes this evidence into a multiaxial model of COVID-19 mental health impacts. Based on existing research, we propose four axes: (1) Exposure to COVID-related events; (2) Personal and social vulnerability, such as previous mental health problems or belonging to a vulnerable group; (3) Time, which accounts for the differential impacts throughout the development of the pandemic; and (4) Context, including healthcare and public policies, and social representations of the illness influencing individual emotional reactions and relevant behaviors. These axes help acknowledge the complexity of communities' reactions and are pragmatic in identifying and prioritizing factors. The axes can provide individual information (i.e., more exposure is harmful) and account for interactions (e.g., exposure in an early phase of the pandemic differs from a later stage). This model contributes to the reflections of the evidence and informs the mental health response to the next pandemic. [ABSTRACT FROM AUTHOR]

The 2019 coronavirus disease (COVID-19) pandemic has had a profound influence on different sectors of society, including health. This study hypothesized a significant impact of the pandemic on the quality of life and psychosocial well-being of urothelial cancer patients, specifically anticipating a decrease in anxiety and depression scores as the pandemic progressed. The primary objectives were to assess longitudinal changes in quality of life indexes, evaluate Healthcare Anxiety and Depression Scale (HADS) score trends over three years (2020–2022), and identify any correlational patterns between the progression of the pandemic and anxiety, depression, and stress levels among this cohort. A cross-sectional analysis was conducted on Eastern Cooperative Oncology Group (ECOG) 1 and Tumor Node Metastasis (TNM) stage 1 bladder cancer patients from the Timis County Emergency Clinical Hospital in Romania. Sixty patients were evaluated each year from 2020 to 2022, utilizing a detailed selection process involving the review of both the hospital database and paper records. Key data included demographic information, medical history, and responses to the Patient Health Questionnaire (PHQ-9), Short Form (SF-36), HADS, and Generalized Anxiety Disorder (GAD-7) questionnaires. A total of 163 completed questionnaires were analyzed, providing insight into various aspects of patients' experiences during the pandemic. Notably, the mean hospitalization days ranged from 3.6 ± 2.1 days in 2020 to 4.0 ± 2.4 days in 2022 (p = 0.663). Concerns that current symptoms might be pandemic-related spiked to 63.5% in 2021, but reduced to 50.9% in 2022, with this fluctuation being significant (p = 0.026). The perception of decreased quality of or accessibility to medical care was significant over the years, with a decline to 52.7% in 2022 (p = 0.033). Quality of life assessments demonstrated an upward trend, from an average score of 55.9 ± 8.9 in 2020 to 59.3 ± 8.8 in 2022 (p = 0.049). Interestingly, anxiety levels, as indicated by the HADS survey, revealed a significant decline from a score of 7.8 in 2020 to 6.5 in 2022 (p = 0.008). On the other hand, GAD-7 scores displayed a downward trend over the years, potentially indicative of developed coping strategies (p = 0.034). This study provides a comprehensive insight into the fluctuating dynamics of psychosocial factors and quality of life among urothelial cancer patients during the pandemic years. It underscores a potential adaptive response, as evidenced by the decrease in anxiety levels and an upward trend in the quality of life scores over the period. These findings highlight the resilience and adaptability of this patient cohort amidst the challenges posed by the pandemic, potentially guiding future interventions and supports in similar health crises. [ABSTRACT FROM AUTHOR]

The manner in which individuals worldwide shared art during the most challenging months of the COVID-19 pandemic stands as one of the most significant instances of creative social resistance in recent history. As a collective tool of resistance against emotional trauma, and as a means to foster a sense of community and well-being, the study of this phenomenon offers a compelling avenue for research into creativity and its social functions. This paper presents a descriptive case study of a successful 50-day collective experience within a neighborhood community in Madrid, Spain, during a period when the city, as a notably exceptional case study for research, bore one of the heaviest burdens of COVID-19 in the world. Data were gathered through in-depth personal interviews and direct observations. Applying a connected approach drawing on the fields of the Sociology of Art and Media Studies, three key findings emerge: (1) participants emphasized shared live artistic performances as the primary catalyst for fostering a sense of community, collective resilience, and overall well-being; (2) their sense of togetherness was further bolstered by digital and media support, as recordings of live performances were shared with loved ones living elsewhere, as well as with journalists and on social networks. This network of communication played a pivotal role in connecting individuals; (3) the combined efforts of both initiatives contributed to the development of a more positive individual and shared narrative surrounding the crisis. [ABSTRACT FROM AUTHOR]

Significant and composite indices for infectious disease can have implications for developing interventions and public health. This paper presents an investment for developing access to further analysis of the incidence of individual and multiple diseases. This research mainly comprises two steps: first, an automatic and reproducible procedure based on functional data analysis techniques was proposed for analyzing the dynamic properties of each disease; second, orthogonal transformation was adopted for the development of composite indices. Between 2000 and 2019, nineteen class B notifiable diseases in China were collected for this study from the National Bureau of Statistics of China. The study facilitates the probing of underlying information about the dynamics from discrete incidence rates of each disease through the procedure, and it is also possible to obtain similarities and differences about diseases in detail by combining the derivative features. There has been great success in intervening in the majority of notifiable diseases in China, like bacterial or amebic dysentery and epidemic cerebrospinal meningitis, while more efforts are required for some diseases, like AIDS and virus hepatitis. The composite indices were able to reflect a more complex concept by combining individual incidences into a single value, providing a simultaneous reflection for multiple objects, and facilitating disease comparisons accordingly. For the notifiable diseases included in this study, there was superior management of gastro-intestinal infectious diseases and respiratory infectious diseases from the perspective of composite indices. This study developed a methodology for exploring the prevalent properties of infectious diseases. The development of effective and reliable analytical methods provides special insight into infectious diseases' common dynamics and properties and has implications for the effective intervention of infectious diseases. [ABSTRACT FROM AUTHOR]

Vaccination strategies remain one of the most effective and feasible preventive measures in combating infectious diseases, particularly during the COVID-19 pandemic. With the passage of time, continuous long-term lockdowns became impractical, and the effectiveness of contact-tracing procedures significantly declined as the number of cases increased. This paper presents a mathematical assessment of the dynamics and prevention of COVID-19, taking into account the constant and time-varying optimal COVID-19 vaccine with multiple doses. We attempt to develop a mathematical model by incorporating compartments with individuals receiving primary, secondary, and booster shots of the COVID-19 vaccine in a basic epidemic model. Initially, the model is rigorously studied in terms of qualitative analysis. The stability analysis and mathematical results are presented to demonstrate that the model is asymptotically stable both locally and globally at the COVID-19-free equilibrium state. We also investigate the impact of multiple vaccinations on the COVID-19 model's results, revealing that the infection risk can be reduced by administrating the booster vaccine dose to those individuals who already received their first vaccine doses. The existence of backward bifurcation phenomena is studied. A sensitivity analysis is carried out to determine the most sensitive parameter on the disease incidence. Furthermore, we developed a control model by introducing time-varying controls to suggest the optimal strategy for disease minimization. These controls are isolation, multiple vaccine efficacy, and reduction in the probability that different vaccine doses do not develop antibodies against the original virus. The existence and numerical solution to the COVID-19 control problem are presented. A detailed simulation is illustrated demonstrating the population-level impact of the constant and time-varying optimal controls on disease eradication. Using the novel concept of human awareness and several vaccination doses, the elimination of COVID-19 infections could be significantly enhanced. [ABSTRACT FROM AUTHOR]

With the emergence of COVID-19 disease in 2019, machine learning (ML) techniques, specifically deep learning networks (DNNs), played a key role in diagnosing the disease in the medical industry due to their superior performance. However, the computational cost of deep learning networks (DNNs) can be quite high, making it necessary to often outsource the training process to third-party providers, such as machine learning as a service (MLaaS). Therefore, careful consideration is required to achieve robustness in DNN-based systems against cyber-security attacks. In this paper, we propose a method called the dropout-bagging (DB-COVIDNet) algorithm, which works as a robust defense mechanism against poisoning backdoor attacks. In this model, the trigger-related features will be removed by the modified dropout algorithm, and then we will use the new voting method in the bagging algorithm to achieve the final results. We considered AC-COVIDNet as the main inducer of the bagging algorithm, which is an attention-guided contrastive convolutional neural network (CNN), and evaluated the performance of the proposed method with the malicious COVIDx dataset. The results demonstrated that DB-COVIDNet has strong robustness and can significantly reduce the effect of the backdoor attack. The proposed DB-COVIDNet nullifies backdoors before the attack has been activated, resulting in a tremendous reduction in the attack success rate from  99.5 %  to  3 %  with high accuracy on the clean data. [ABSTRACT FROM AUTHOR]

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset's unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system's improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed significant challenges in accurately diagnosing the disease, as severe cases may present symptoms similar to pneumonia. Real-Time Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is the conventional diagnostic technique; however, it has limitations in terms of time-consuming laboratory procedures and kit availability. Radiological chest images, such as X-rays and Computed Tomography (CT) scans, have been essential in aiding the diagnosis process. In this research paper, we propose a deep learning (DL) approach based on Convolutional Neural Networks (CNNs) to enhance the detection of COVID-19 and its variants from chest X-ray images. Building upon the existing research in SARS and COVID-19 identification using AI and machine learning techniques, our DL model aims to extract the most significant features from the X-ray scans of affected individuals. By employing an explanatory CNN-based technique, we achieved a promising accuracy of up to 97 % in detecting COVID-19 cases, which can assist physicians in effectively screening and identifying probable COVID-19 patients. This study highlights the potential of DL in medical imaging, specifically in detecting COVID-19 from radiological images. The improved accuracy of our model demonstrates its efficacy in aiding healthcare professionals and mitigating the spread of the disease. [ABSTRACT FROM AUTHOR]

Mutations and the glycosylation of epitopes can convert immunogenic epitopes into non-immunogenic ones via natural selection or evolutionary pressure, thereby decreasing their sensitivity to neutralizing antibodies. Based on Thomas Francis's theory, memory B and T cells induced during primary infections or vaccination will freeze the new mutated epitopes specific to naïve B and T cells from the repertoire. On this basis, some researchers argue that the current vaccines derived from the previous strains of the SARS-CoV-2 virus do not increase immunity and may also prevent the immune response against new epitopes. However, evidence shows that even if the binding affinity is reduced, the previous antibodies or T cell receptors (TCRs) can still bind to this new epitope of the Beta, Gamma, and Delta variant if their concentration is high enough (from a booster injection) and neutralize the virus. This paper presents some convincing immunological reasons that may challenge this theory and argue for the continuation of universal vaccination to prevent further mutations of the SARS-CoV-2 virus. Simultaneously, the information presented can be used to develop vaccines that target novel epitopes or create new recombinant drugs that do not lose their effectiveness when the virus mutates. [ABSTRACT FROM AUTHOR]

The usual lives have been changed since the COVID-19 Outbreak took place. Elderly people suffer as much as others and their lives have been impacted a lot. This paper aims to analyze the social network use and the recreation and socialization activity participation before and after the COVID-19 pandemical appearance in long-term care institutions. In the empirical side of this research, the authors of this paper sent an email questionnaire to long-term care institutions located in Rio de Janeiro city, Brazil. In the theoretical side of this research, an investigation was carried out through the scientific databases. Data were analyzed using the programming languages Python, Excel, and IBM SPSS Statistics. As a result, this paper shows an apparent increase in social network use and a drop in recreation and socialization activity participation after the COVID-19 pandemical appearance in long-term care institutions. The presented findings are the first step, which can be part of future studies analyzing the potential growth in social network use among elderly people by comparing elderly residents with non-residents in the long-term care institutions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the ant colony and Physarum Polycephalum algorithms. The positive feedback mechanism is used to find the globally optimal path. The crossover and mutation operations of the genetic algorithm are introduced into the path search mechanism for the first time. The Van der Waals force is applied to the pheromone updating mechanism. Simulation results show that the improved algorithm has advantages in quality and speed of solution compared with other mainstream algorithms. This paper provides fast and accurate route methods for solving the Traveling Salesman Problem first and a delivery scheme is also presented for UAVs to realize "contactless delivery" to users in the Changchun Mingzhu District during the COVID-19 epidemic, which confirms the practicability and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper explores two pandemic-era projects conducted by the Museum and Archives of North Vancouver (MONOVA) using a participatory archives framework during the COVID-19 pandemic. #NorthVanStories, a rapid-respond social media collecting project, ran from May to September 2020 and received over 200 submissions. #NorthVanStories–Living Histories, a digital video oral history project, ran from summer 2020 to fall 2021 and produced five videos with seven storytellers from diverse and vulnerable communities. This paper discusses the power of collaborative documenting, storytelling, and memory-making to foster connectivity, increase empathy, and build resilience. It highlights the benefits of working collaboratively with organizations and communities; successful approaches to planning and outreach activities; the importance of flexibility and sustainability when working with communities in times of crisis; and how to ensure the future relevance of archives by being responsive to contemporary events and community interests. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in an influx of misinformation surrounding the virus and its origins. This paper examines the negative consequences of neoliberal self-care discourses related to COVID-19 that contributed to the disinfodemic, focusing on the wellness industry. Some health gurus and wellness instructors promoted lifestyle adjustments and self-care to prevent contracting the virus and sometimes blamed COVID-19 patients for contracting it. These claims contributed to vaccine hesitancy and pseudo-scientific beliefs. The misinformation was attractive partly because it promised individual control and independence from mainstream (scientific) advice. These discourses existed before the pandemic but were amplified within the wellness community after the COVID-19 pandemic. The paper concludes by exploring the feminist notions of care and solidarity as a counter to the neoliberal notion of self-care. [ABSTRACT FROM AUTHOR]

The Bangladesh government issued a lockdown throughout the country from March–May 2020 in response to the COVID-19. The sudden lockdown caused economic ruptures across the country due to job loss. We conducted a comprehensive analysis of the outbreak through 40 in-depth interviews with men and women living in three Dhaka informal settlements from January to November 2021 to identify gaps to mitigate negative downstream effects of global pandemic policies. In this paper, we explore the critical importance of social networks as coping mechanisms for those who lost livelihood due to COVID-19 lockdown. Due to the congested living conditions in informal settlements, many established residents foster close, trusting relationships, and a strong sense of community. Formal and informal networks in urban slums, whether reciprocal or strategic, played an integral role as a way of coping during times of scarcity. We found limited analysis in public health literature on the resilience of these social networks and its impact on health and wellbeing. Our paper attempts to unpack the ways our respondents drew on their own social networks to combat the socio-economic and emotional health challenges brought on by a lack of adequate formalized support as part of the pandemic response. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted. [ABSTRACT FROM AUTHOR]

Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease. [ABSTRACT FROM AUTHOR]

The volume of residential travel with different purposes follows relatively stable patterns in a specific period and state; therefore, it can reflect the operating status of urban traffic and even indicate urban vitality. Recent research has focused on changes in the spatiotemporal characteristics of urban mobility affected by the pandemic but has rarely examined the impact of COVID-19 on the travel conditions and psychological needs of residents. To quantitatively assess travel characteristics during COVID-19, this paper proposed a method by which to determine the purpose of residential travel by combining urban functional areas (UFAs) based on machine learning. Then, the residential travel structure, which includes origin–destination (OD) points, residential travel flow, and the proportion of flows for different purposes, was established. Based on taxi trajectory data obtained during the epidemic in Nantong, China, the case study explores changes in travel flow characteristics under the framework of the residential travel structure. Through comparison of the number and spatial distribution of OD points in the residential travel structure, it is found that residential travel hotspots decreased significantly. The ratios of commuting and medical travel increased from 43.8% to 45.7% and 7.1% to 8.1%, respectively. Conversely, the ratios of other travel types all decreased sharply. Moreover, under Maslow's hierarchy of needs model, further insights into the impacts of COVID-19 on changes in residential psychological needs are discussed in this paper. This work can provide a reference for decision makers to cope with the change in urban traffic during a public health emergency, which is beneficial to the sustainable healthy development of cities. [ABSTRACT FROM AUTHOR]

This paper considers the diverse public good characteristics of COVID-19 activities along with their strategic implications. The underlying aggregator technologies, which relate individual contributions to the amount consumed, affect the prognosis for the supply of COVID-related activities. Weakest-link activities assume a particularly pivotal role in curbing the spread of COVID-19. For instance, the propagation of COVID-19 through new strains is disproportionately influenced by those countries with the smallest vaccination rates or least isolation actions. Diverse income distribution among at-risk countries raises the need to "shore up" weakest-link countries' provision to lift global supply. Generally, shoring-up actions result in a Prisoner's Dilemma with unfavorable collective action prospects. As the number of countries requiring shoring up increases, the less favorable is the prospect for addressing provision shortfalls. Also, as the number of capable countries to do the shoring up increases, the prospect for successful action diminishes. The paper also examines the strategic implications of other aggregators—e.g., best shot and better shot—associated with COVID-inhibiting actions. To address best-shot anti-COVID actions, countries must pool or coordinate actions to meet a threshold. A host of institutions—e.g., the World Health Organization or public-private partnerships—can facilitate shoring-up weakest-link activities or coordinating best-shot actions. [ABSTRACT FROM AUTHOR]