Search

As a result of the spread of the coronavirus (COVID-19), thousands of small companies around the world have been severely disrupted. Many business professionals, particularly entrepreneurs, suffer from the unprecedented magnitude of the lockdown of social activities, which is combined with limits on individual mobility. This study investigates the resilience of entrepreneurs—which is characterized by hardiness, resourcefulness, and optimism—as well as the relationship between resilience and creative performance. Additionally, the mediating role of institutional orientation is investigated in order to highlight how contextual factors influence this relationship. Using a quantitative study approach and structural equation modeling data analysis technique, 390 entrepreneurs were investigated, and the analyzed data demonstrate that entrepreneurs' ability to persevere in the face of adversity is strongly related to their ability to innovate, with institutional orientation serving as a partial mediating variable. Implications and future research opportunities are also explored in the paper. [ABSTRACT FROM AUTHOR]

The paper presents the results of sociological qualitative research on safety management in work organization. The focus was on how to adapt the workforce to the sanitary regime during the time of the COVID-19 pandemic in the lignite mining and energy sector in the Bełchatów poviat (Łódź voivodeship, Poland). The study aimed to identify the dimensions of safety and the work culture created in crisis conditions as perceived by workers. Based on the conducted analysis, the process of adapting employees to the new rules of work organization was reported, including the transition from fear and anxiety to control over threats and negative emotions. The essence of the research was to support institutional memory to consolidate the existing knowledge and use it in the future. [ABSTRACT FROM AUTHOR]

The end of the acute phase of the COVID-19 pandemic is near in some countries as declared by World Health Organization (WHO) in January 2022 based on some studies in Europe and South Africa despite unequal distribution of vaccines to combat the disease spread globally. The heterogeneity in individual age and the reaction to biological and environmental changes that has been observed in COVID-19 dynamics in terms of different reaction to vaccination by age group, severity of infection per age group, hospitalization and Intensive Care Unit (ICU) records show different patterns, and hence, it is important to improve mathematical models for COVID-19 pandemic prediction to account for different proportions of ages in the population, which is a major factor in epidemic history. We aim in this paper to estimate, using the Usher model, the lifespan loss due to viral infection and ageing which could result in pathological events such as infectious diseases. Exploiting epidemiology and demographic data firstly from Cameroon and then from some other countries, we described the ageing in the COVID-19 outbreak in human populations and performed a graphical representation of the proportion of sensitivity of some of the model parameters which we varied. The result shows a coherence between the orders of magnitude of the calculated and observed incidence numbers during the epidemic wave, which constitutes a semi-quantitative validation of the mathematical modelling approach at the population level. To conclude, the age heterogeneity of the populations involved in the COVID-19 outbreak needs the consideration of models in age groups with specific susceptibilities to infection. [ABSTRACT FROM AUTHOR]

Habitat fragmentation is widely recognized as a contributor to the decline of biodiversity, with amphibians one of the key groups impacted. To understand the effects of habitat fragmentation on amphibian populations requires long-term data sets showing population trends. In this paper, road mortalities were employed as proxies to describe long-term numbers of four populations of the spiny toad Bufo spinosus in western France during a 17-year period. Road mortalities were found during all months in all populations but were most frequent during October, November and December, the main migratory period. Large females were found significantly more frequently during these migration months, forming 45% of the total sample, compared with their presence from January to September (34.4%). The long-term trends were evaluated using regression analysis of the logarithmic (loge) transforms of annual counts as dependent variables against year as the independent variables. All coefficients showed no significant departure from the 0 hypothetical coefficients, indicative of population stability. This was supported by jackknife analysis, which showed good agreement of the pseudo-regression coefficients with the true equations. Stepwise regression of potential climate impacts on toad numbers suggested rainfall levels in October adjusted to 2- and 3-year lags were involved in driving population change. Road mortality counts were also made during 2020 and 2021 when human movement restrictions were in place due to the COVID-19 pandemic. To estimate the potential impact on this disturbance in the methodology, the Poisson distribution was used to estimate potential differences between what would have been expected counts and the observed counts. The results indicate that the observed mortalities were significantly lower than expected in all four populations. [ABSTRACT FROM AUTHOR]

The heavy economic, social, and psychological toll of pandemic lockdowns around the world and their disproportionate effect on women are widely acknowledged, but different socioeconomic backgrounds and contexts may influence the degree to which stay-at-home measures impact their lives. Additionally, knowing that violence against women tends to increase during times of crisis, we are testing if the additional burden of victimization represents an added load to the perceived social impacts of the lockdown. Using 2021 survey data from a random sample of 1541 Portuguese women, the paper explores, through logistic regression models, the social impact of the lockdown on the lives of women, its socioeconomic determinants, and the role played by violence against women during the pandemic. The results show that the COVID-19 pandemic lockdown did not equally affect all facets of women's social lives, and women with higher education status and that experienced income reductions due to the measures taken to control the pandemic are more prone to experience a more severe negative impact of the lockdown on the various facets of their lives. Additionally, having been a victim during the pandemic partially mediates the effect of education and income reduction on the social outcomes of the lockdown. [ABSTRACT FROM AUTHOR]

Spain was one of the countries in which more severe lockdown policies were imposed during the second term of 2020 to mitigate the unprecedented health crisis. The measures restricted citizens' mobility, obliging families to stay confined at homes for 99 days since 15 March 2020. The measures created a number of challenges that affect the family climate. This paper aims to empirically analyse how the family climate in Spain has been affected by COVID-19. The family climate assessment was based on an online questionnaire answered by 2034 citizens. A multi-criteria decision-making method rooted in fuzzy logic and TOPSIS, and a fuzzy clustering method, are applied to analyse the effects of the COVID-19 on the family climate. The fuzzy clustering method reveals that there are three different family climate profiles, namely (1) extreme positive, (2) extreme negative, and (3) intermediate. Our results show that some traits affect having a more or less positive family climate. The authors discuss the main contributions and the policy implications that could provide insights into future measures. [ABSTRACT FROM AUTHOR]

The course of COVID-19 infection may be complicated by a variety of neurological manifestations. Since the inner ear is vulnerable to viruses, sensorineural hearing loss (SNHL) has been reported to occur following the SARS-CoV-2 infection, often resulting in long-term morbidity and worsening the quality of life. The interest in how the virus affects the inner ear has gradually increased since the pandemic's spread, but little is still known about the SNHL potentially caused by SARS-CoV-2. The aim of this paper is to evaluate the possible association between SNHL and COVID-19 infection, through a systematic literature review. Currently available data suggest that SARS-CoV-2 may hamper cochlear function; however, available reports are still limited. Large cohort and prospective studies are necessary to evaluate the long-term effects of this viral infection in the inner ear. [ABSTRACT FROM AUTHOR]

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. [ABSTRACT FROM AUTHOR]

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway's (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 lockdowns imposed new challenges to couples who were planning to conceive. In this research paper, we aimed to study the perceptions of women in Jordan during the pandemic regarding fertility behavior, the desire to use assisted reproductive technology (ART) and the awareness and beliefs of potential risks related to conception. Methods: A validated online-based questionnaire was distributed to women from April–May 2020, Statistical analysis was performed using the statistical software SPSS version 22 and R software (2020); p values ≤ 0.05 were considered statistically significant. Results: The total number of participants was 814 women, with 78.2% of the participants (58.7% fertile and 76.6% infertile) believing that pregnancy during the COVID-19 pandemic could be risky. Among them, 16% and 40%, respectively, were trying to conceive during the pandemic, and 97.4% and 89.9%, respectively, were not willing to use ART if needed during the pandemic. Young, nulliparous women who were married for less than one year were significantly associated with the desire to conceive during the COVID-19 pandemic. Conclusion: This study concluded that the fertility behavior of women in Jordan changed during the pandemic, and their desire for natural conception and for using ART declined, as they believed that there were potential risks related to conceiving during the pandemic. However, the effect was greater among the general fertile population than the infertile. [ABSTRACT FROM AUTHOR]

Modeling the number of individuals in different states is a principal tool in the event of an epidemic. The natural transition of individuals between possible states often includes deliberate interference such as isolation or vaccination. Thus, the mathematical model may need to be re-calibrated due to various factors. The model considered in this paper is the SIRD epidemic model. An additional parameter is the moment of changing the description of the phenomenon when the parameters of the model change and the change is not pre-specified. Detecting and estimating the moment of change in real time is the subject of statistical research. A sequential (online) approach was applied using the Bayesian shift point detection algorithm and trimmed exact linear time. We show how methods of analysis behave in different instances. These methods are verified on simulated data and applied to pandemic data of a selected European country. The simulation is performed with a social network graph to obtain a practical representation ability. The epidemiological data used come from the territory of Poland and concern the COVID-19 epidemic in Poland. The results show satisfactory detection of the moments where the applied model needs to be verified and re-calibrated. These show the effectiveness of the proposed combination of methods. [ABSTRACT FROM AUTHOR]

Since November 2019, each country in the world has been affected by COVID-19, which has claimed more than four million lives. As an infectious disease, COVID-19 has a stronger transmission power and faster propagation speed. In fact, environmental noise is an inevitable important factor in the real world. This paper mainly gives a new random infectious disease system under infection rate environmental noise. We give the existence and uniqueness of the solution of the system and discuss the ergodic stationary distribution and the extinction conditions of the system. The probability density function of the stochastic system is studied. Some digital simulations are used to demonstrate the probability density function and the extinction of the system. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally, resulting in the ongoing coronavirus pandemic. The most important type of COVID-19 confrontation is the use of polymerase chain reaction testing (PCR) to detect if a person is healthy or infected with COVID-19. Many countries released different numbers about the total cases of infected persons with the pandemic based on the number of total PCRs and other statistical numbers related to this disease. The total case numbers for some countries were very promising, such that they implied that these countries were to recover soon from these diseases. At other times, some countries overestimated the total number of cases or deaths to get financial aid. Therefore, there is a need to verify and evaluate these numbers by using machine-learning algorithms that proved to be efficient in solving many problems. The convolutional neural network (CNN) is an advanced machine-learning algorithm that was deployed to detect COVID-19 from different medical images such as X-ray images. However, CNN was not used to validate the published COVID-19 statistical data. In this paper, we adapted the self-organizing UNet (SO-UNet) in the cooperative convolutional neural network (CCNN) model to detect the accuracy of the reported COVID-19 statistics. The detection is based on using COVID-19 statistical variables that are reported by reliable and credible databases provided by known global organizations. These variables are used to create multi-dimension images to train and test the CCNN model. The results showed that reported important statistics for COVID-19 could be verified using the CCNN model. It also showed that the verified data coincided with the verification reported by the most important and trusted organizations in the world. Finally, the comparison of the new model to Benford's law outcome proved the model's credibility and efficiency in validating COVID-19 reported data. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) has exposed the unpreparedness of governments in their capacities to prevent, detect, and respond to emerging infectious diseases. Many healthcare systems have been overburdened and the coordinated efforts in different countries have focused on containment and mitigation, with varying degrees of success. A delay in the detection of and response to infectious diseases can lead to the overburdening of already challenged health systems. Containment strategies, such as social distancing, contact tracing, quarantining of exposed individuals and lockdowns, can help control the spread of the infection in communities. Still, long-term solutions should be sought to counter future outbreaks. In this paper, we focus on Zimbabwe to identify and discuss public health strategies that can result in an effective response to future infectious disease outbreaks. We consider potential solutions to facilitate early detection, control, and mitigation of any similar emerging infectious disease. We argue that sustained financial support in public health infrastructure, both locally and nationally, integrated surveillance response systems, and improved communication and research within and across public and private sectors can be instrumental in limiting the damage caused by future outbreaks. [ABSTRACT FROM AUTHOR]

The present study examines the ways in which the Filipino Christian value of pakikipagkapwa (fellowship) can be seen and experienced in modern society. Using empirical phenomenology, this paper aims to (re)imagine the ways of cultivating ways of dialogue with religious others while understand the meaning of pakikipagkapwa (fellowship). This study explores the contemporary notions and practices of pakikipagkapwa among select Filipino Christians and how such cultural value fosters interreligious dialogue. Moreover, the study investigates the importance of dialogue between religious actors as they navigate the uncharted waters of the COVID-19 pandemic. The authors contend that pakikipagkapwa and interreligious dialogue build communities, support social cohesion, and help religious actors find meaning in difficult circumstances. [ABSTRACT FROM AUTHOR]

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate ( β 1 ) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model. [ABSTRACT FROM AUTHOR]

This paper explores the impact of the COVID-19 pandemic on the connectedness of stock indexes in the group of developed and emerging economies known as the ASEAN+3. We derived our empirical findings from the Diebold and Yilmaz (DY12) and Baruník and Křehlík (BK18) spillover methods, using daily data from 10 May 2005 to 24 February 2021. We show that the COVID-19 pandemic has had a bigger impact on the return and volatilities of ASEAN+3 stock markets than previous economic turmoil, such as the 2008 global financial crisis and the 2009–2012 European debt crisis. Using a frequency domain methodology, we find evidence that return spillovers mostly occur in the short-term, while volatility connectedness is more pronounced in the long-term. The Singapore stock market primarily acts the as top transmitter in returns and volatilities, whereas Vietnam has become the top receiver of shocks in returns. We also demonstrate that it is possible to replicate the frequency-domain connectedness measures of BK18 with a DY12 methodology. Using a series decomposed with a wavelet-based approach, we find that the total spillover indices for short-, medium-, and long-term frequencies computed with the DY12 approach are comparable to the within connectedness indices of BK18. Our results have important policy implications for investors, regulators, and policy makers. [ABSTRACT FROM AUTHOR]

Between 13 November 2020 and 11 February 2021, an online national survey of 2003 Asian Australians was conducted to measure the type and frequency of self-identified Asian Australians' experiences of racism during the COVID-19 pandemic. The survey also aimed to gauge the relationships between racist experiences and targets' mental health, wellbeing and sense of belonging. In this paper, we report findings on the type and frequency of online racist experiences and their associations with mental health, wellbeing and belonging. The survey found that 40 per cent of participants experienced racism during the COVID-19 pandemic. Within that group, 66 per cent experienced racism online. The demographic pattern of those most likely to experience online racism were younger age groups, males, those born in Australia, English speakers at home, non-Christians, and migrants who have been in Australia less than 20 years. Analysis also found a strong correlation between Asian Australians' experiences of online racism and poor mental health, wellbeing and belonging. The relationship between experiencing racism, non-belonging and morbidity were more pronounced for those who experienced online racism compared to those who experienced racism in other offline contexts. This points to the corrosive nature of online racism on social cohesion, health and belonging. [ABSTRACT FROM AUTHOR]

The battle against COVID-19 has entered a new phase with Rehabilitation Centres being among the major players, because the medical outcome of COVID-19 patients does not end with the control of pulmonary inflammation marked by a negative virology test, as many patients continue to suffer from long-COVID-19 syndrome. Exercise training is known to be highly valuable in patients with cardiac or lung disease, and it exerts beneficial effects on the immune system and inflammation. We therefore reviewed past and recent papers about exercise training, considering the multifactorial features characterizing post-COVID-19 patients' clinical conditions. Consequently, we conceived a proposal for a post-COVID-19 patient exercise protocol as a combination of multiple recommended exercise training regimens. Specifically, we built pre-evaluation and exercise training for post-COVID-19 patients taking advantage of the various programs of exercise already validated for diseases that may share pathophysiological and clinical characteristics with long-COVID-19. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) has had an immense impact on the treatment protocols of orthopedic and trauma departments, yet its specific effect on mortality in patients with hip fractures due to possible surgical delays is still unclear. The purpose of this paper was to investigate whether the COVID-19 pandemic worsened the mortality rate of hip fracture patients. Patients and methods: This study comprised 175 prospectively included patients who (1) suffered from hip fractures, (2) presented during the Austrian state of emergency period from 15 March 2020 to 30 May 2021, and (3) were admitted to a level I trauma center. This cohort was compared with a retrospective control group of 339 patients admitted for hip fractures during the same timeframe in 2017, 2018, and 2019. Results: An admission reduction of 22% in the COVID period compared with the pre-COVID period was evident (p = 0.018). The 30-day mortality rate was 14.67% (pre-COVID) compared with 15.18% (p = 0.381). No differences in surgical complication rates or relationships between comorbidity burden and survival were observed. There were no significant changes in demographic variables, except for admission rate, gender (p = 0.013), and place of accident (p = 0.049). Conclusion: Surgeons should be reassured to take COVID-19 precautions, as this study did not show higher perioperative mortality due to COVID-19 measures. Under the current circumstances, with possibly reduced surgical and hospital bed capacities, it is expected that hip fractures may continue to require a high degree of resources. [ABSTRACT FROM AUTHOR]

Simple Summary: In the past two years, the COVID-19 incidence curves and reproduction number R t have been the main metrics used by policy makers and journalists to monitor the spread of this global pandemic. However, these metrics are not always reliable in the short term, because of a combination of delay in detection, administrative delays and random noise. In this article, we present a complete model of COVID-19 incidence, faithfully reconstructing the incidence curve and reproduction number from the renewal equation of the disease and precisely estimating the biases associated with periodic weekly bias, festive day bias and residual noise. The sanitary crisis of the past two years has focused the public's attention on quantitative indicators of the spread of the COVID-19 pandemic. The daily reproduction number R t , defined by the average number of new infections caused by a single infected individual at time t, is one of the best metrics for estimating the epidemic trend. In this paper, we provide a complete observation model for sampled epidemiological incidence signals obtained through periodic administrative measurements. The model is governed by the classic renewal equation using an empirical reproduction kernel, and subject to two perturbations: a time-varying gain with a weekly period and a white observation noise. We estimate this noise model and its parameters by extending a variational inversion of the model recovering its main driving variable R t . Using R t , a restored incidence curve, corrected of the weekly and festive day bias, can be deduced through the renewal equation. We verify experimentally on many countries that, once the weekly and festive days bias have been corrected, the difference between the incidence curve and its expected value is well approximated by an exponential distributed white noise multiplied by a power of the magnitude of the restored incidence curve. [ABSTRACT FROM AUTHOR]

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user's home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease. [ABSTRACT FROM AUTHOR]

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large. [ABSTRACT FROM AUTHOR]

Because of the COVID-19 pandemic, religious services worldwide were forced to migrate online. This phenomenon is still yet to be properly investigated, especially in the context of religious and ethnic minorities: a research gap that this paper aims to address. Herein, 18 semi-structured in-depth interviews with members of the Russian community in South Tyrol served as methodological tool to explore: (1) the role of the Orthodox religion and (2) of digital media for the community and, finally, (3) how its members enacted religion during the COVID-19 pandemic. Empirical results show how community members, despite being highly familiar with digital tools and technology-based solutions, deliberately chose not to use digital media to search for religious content or to attend religious services. Against the backdrop of a global pandemic and an increasingly digitised world, the specific example of the Russian community in South Tyrol helps to better understand how migrant communities live and enact religion. The present study aims at contributing to the emerging field of the study of (digital) religion and embodiment of religion from a minority perspective. [ABSTRACT FROM AUTHOR]

A pneumonia outbreak was primarily reported in the fall of 2019 in Wuhan, Hubei province, China, with the identity SARS-CoV-2, a novel coronavirus. It quickly grew from a local epidemic to a global pandemic and was declared a public health emergency by the WHO. A total of three prominent waves were identified across the globe, with a slight temporal variability as per the geographical locations, and has impacted several sectors which connect the world. By March 2022, the coronavirus had infected 444.12 million people and claimed 6.01 million human lives worldwide, and these numbers have not yet stabilized. Our paper enlightens readers on the seven strains of human coronaviruses, with special emphasis on the three severe deadliest outbreaks (SARS-2002, MERS-2012, and COVID-19). This work attempts a comprehensive understanding of the coronavirus and its impact on the possible sectors that link the world through the economic chain, climate conditions, SDGs, recycling of the event, and mitigations. There are many points that are raised by the authors in the possible sectors, which are emerging or are as yet unnoticed and thus have not been taken into consideration. This comprehension will leave sets of new challenges and opportunities for the researchers in various streams, especially in earth sciences. Science-integrated research may help to prevent upcoming disasters as a by-product of (existing) epidemics in the form of coronavirus. [ABSTRACT FROM AUTHOR]

Prices of oil and other commodities have fluctuated wildly since the outbreak of the COVID-19 pandemic. It is crucial to explore the causes of price fluctuations and understand the source and path of risk contagion to better mitigate systemic risk and maintain economic stability. The paper adopts the method of network topology to examine the path of risk contagion between China's and foreign commodities, focusing on the dynamic evolution and transmission mechanism of risk contagion during the pandemic. This research found that among China's commodities, energy, grain, and textiles are net recipients of risk contagion, while chemical products and metals are net risk exporters. Among international commodities, industries have positive risk spillover effects on metals and textiles. During the first phase of the pandemic, China's commodities were the main exporters of risk contagion. However, international industries and metals became the main risk exporters and exerted risk spillover on China's commodities in the second phase of the pandemic. Moreover, based on total volatility spillover index of commodities, the risk contagion among the commodities follows three paths: "interest rate → commodities → money supply", "China's economic expectation → commodities → foreign economic expectation", and "commodities → consumer confidence". [ABSTRACT FROM AUTHOR]

The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research. [ABSTRACT FROM AUTHOR]

The surveyof traffic intensity is used to obtain information on the number of vehicles on roads during the day. Subsequently, it is possible to derive from this the daily, weekly, and other road traffic intensity information. This survey represents the basis for the calculation of the annual average daily traffic volume and the basic characteristics of traffic flow. The COVID-19 pandemic has caused extensive economic and social damage around the world. These damages have also affected traffic. Changes in traffic behavior have mainly affected the reduction in traffic intensity on road networks. Thanks to the reduction in the demand for transport, there has also been a significant reduction in traffic delays, fuel consumption and emissions. An examination of changes in traffic intensity took place at a selected intersection in 2019, 2020 and 2021. This paper describes the effects of reducing the traffic intensity, fuel consumption and emissions obtained by microsimulation. The results obtained confirmed the reduction in traffic, which also contributed to a significant reduction in vehicle delays. [ABSTRACT FROM AUTHOR]

Museums increasingly recognize the need to address advances in digital culture which impact the expectations and needs of their audiences. Museum collections of real objects need to be presented both on their own premises and digitally online, especially as digital and social media becomes more and more influential in people's everyday lives. From interdisciplinary perspectives across digital culture, art, and technology, we investigate these challenges magnified by advances in digital and computational media and culture, looking particularly at recent and relevant reports on changes in the ways museums interact with the public. We focus on human digital behavior, experience, and interaction in museums in the context of art, artists, and human engagement with art, using the observational perspectives of the authors as a basis for discussion. Our research shows that the COVID-19 pandemic has accelerated many of the changes driving museum transformation, about which this paper presents a landscape view of its characteristics and challenges. Our evidence shows that museums will need to be more prepared than ever to adapt to unabated technological advances set in the midst of cultural and social revolution, now intrinsic to the digital landscape in which museums are inevitably connected and participating across the global digital ecosystem where they inevitably find themselves entrenched, underscoring the central importance of an inclusive integrative museum model between physical and digital reality. [ABSTRACT FROM AUTHOR]

Simple Summary: The family of coronaviruses comprises a diverse set of strains and variants which cause diseases from the common cold to COVID-19. Moreover, they infect a wide array of hosts from bats, camels, birds, to humans. Studying coronaviruses through the lens of host specificity provides a unique perspective to understanding the evolution, diversity and dynamics of this family. In particular, this can reveal groups of different hosts infected by similar strains, giving clues on strains which were more likely to have evolved to jump from one host to another. In this work, we frame host specificity as a classification task, in designing a very compact numerical representation of the spike sequences of different coronaviruses. Based on this numerical representation, classification methods are able to detect the target host with high accuracy. Such an approach can used to efficiently scale to large volumes of sequences, in order to unveil trends in the host specificity of different coronavirus strains. The study of host specificity has important connections to the question about the origin of SARS-CoV-2 in humans which led to the COVID-19 pandemic—an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona)viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating, and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is important in determining host specificity, since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among birds, bats, camels, swine, humans, and weasels, to name a few. We propose a feature embedding based on the well-known position weight matrix (PWM), which we call PWM2Vec, and we use it to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications, such as determining protein function and identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs from viral sequences to generate fixed-length feature vector representations, and use them in the context of host classification. The results on real world data show that when using PWM2Vec, machine learning classifiers are able to perform comparably to the baseline models in terms of predictive performance and runtime—in some cases, the performance is better. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus. Finally, we perform some statistical analyses on these results to show that our embedding is more compact than the embeddings of the baseline models. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the COVID-19 crisis in Russia, France, Germany, and the UK, as covered by the Russian state media outlet RT (formerly Russia Today). I view the RT coverage through the prism of biopolitics and critical discourse analysis (CDA) to demonstrate multiple discrepancies in its "post-truth" knowledge production strategies. I argue that these strategies aim to expose the hybrid and controversial nature of biopolitical governance in Western democracies during the COVID-19 pandemic as they struggle to strike a balance between imposing social restrictions and safeguarding public health. I also show how the (post)liberal biopolitical debate on personal responsibility and state resilience in times of emergency could be applied by authoritarian regimes for self-description. [ABSTRACT FROM AUTHOR]

Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and removing restrictions on a system with a given latency as represented by an epidemic outbreak (and formally comparable with mechanical inertia), may create critical instabilities, overshoots, and strong oscillations in the number of infected people around the desirable set-point, defined in a practical way as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to any change of the input control variable can be reasonably achieved using a proportional-integral-derivative controller (PID), which is a widely used technique in various physics and technological applications. In this paper, this control theory to is proposed to be applied epidemiology, to understand the reaction of COVID-19 propagation to social restrictions and to reduce epidemic damages through the correct tuning of the containment policy. Regarding the synthesis of this interdisciplinary approach, the extended to the susceptible-infectious-recovered (SIR) model name "SIR-PID" is suggested. [ABSTRACT FROM AUTHOR]

COVID-19 has brought not only fear and anxiety, but also legitimate restrictions of communication and consequential touch-deprivation in our daily lives. Couples' relational wellbeing continues to be impacted by these COVID-19 related stressors. Protecting both personal and relational wellbeing is therefore particularly important at this time. Using a preventative intervention approach, the current paper argues the theoretical benefit of the Positive Massage programme and reports a qualitative analysis of stressed but healthy couples' experience of engaging in the programme. Thirty-four participants completed 3 weekly classes and home-based practice of massage exchange. Data from an open text online questionnaire completed every week of the programme and again 3 weeks afterwards were analysed using thematic analysis. The identified themes included "holistic stress relief", "relationship-promotion", and "selves-care skill". Couples perceived Positive Massage as an effective mutual support skill to relax and help one another by de-stressing, both emotionally and physically through verbal and nonverbal communication, creating gratitude, deeper connection and self-efficacy via quality time together and pleasurable touch. Theoretically and experientially, Positive Massage can be an effective preventative selves-care skill. Promoting the concept of selvescare and its practical application through Positive Massage would be beneficial for couples' personal and relational wellbeing in a touch-deprived era. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a member of the genus Betacoronavirus in the family Coronaviridae, possesses an unusually large single-stranded viral RNA (ssvRNA) genome of about ~29,811 nucleotides (nt) that causes severe and acute respiratory distress and a highly lethal viral pneumonia known as COVID-19. COVID-19 also presents with multiple ancillary systemic diseases and often involves cardiovascular, inflammatory, and/or neurological complications. Pathological viral genomes consisting of ssvRNA, like cellular messenger RNA (mRNA), are susceptible to attack, destruction, neutralization, and/or modulation by naturally occurring small non-coding RNAs (sncRNAs) within the host cell, some of which are known as microRNAs (miRNAs). This paper proposes that the actions of the 2650 known human miRNAs and other sncRNAs form the basis for an under-recognized and unappreciated innate-immune regulator of ssvRNA viral genome activities and have implications for the efficiency of SARS-CoV2 invasion, infection, and replication. Recent research indicates that both miRNA and mRNA abundance, speciation, and complexity varies widely amongst human individuals, and this may: (i) In part explain the variability in the innate-immune immunological and pathophysiological response of different human individuals to the initiation and progression of SARS-CoV-2 infection in multiple tissue types; and (ii) further support our understanding of human biochemical and genetic individuality and the variable resistance of individuals to ssvRNA-mediated viral infection and disease. This commentary will briefly address current findings and concepts in this fascinating research area of non-coding RNA and innate-immunity with special reference to natural host miRNAs, SARS-CoV-2, and the current COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 (COVID-19) has been changing the world since December 2019. A comprehensive search into many COVID-19 treatment guidelines was conducted and reported in this article. This is a review paper to probe differences in COVID-19 managing strategies and explore the most common treatment plans among countries. Published guidelines from 23 countries and three references guidelines—until the end of 2020—were included in this article. The majority of COVID-19 treatment options were reported in this review and it includes antiviral drugs, antimalarial drugs, antibiotics, corticosteroids, immunotherapy, anticoagulants, and other pharmacological treatment. The presence of such information from different countries in a single comprehensive review article could help in understanding and speculation of variation in the recommended treatment in each country. This might be related to the cost of medications, the access to the medications, availability of medication that could potentially be useful in managing COVID-19 cases, and the availability/capacity of healthcare facilities. Finally, although there are various treatment groups listed in the published therapeutic guidelines worldwide, unfortunately, there is no evidence for effectiveness of most of these medications in reducing the COVID-19 mortality curve over more than one year of this global pandemic. [ABSTRACT FROM AUTHOR]

This study provides a descriptive analysis of the Lègami/Legàmi service, a free psychological support service in maternal and childcare, offered through the internet and by telephone that was initiated by the Italian Society of Pediatric Psychology (S.I.P.Ped.) during the COVID-19 medical emergency as an act of solidarity, first independently, and then in collaboration with the Italian Ministry of Health. This paper presents findings related to the "universe" of people who called the toll-free service, from the sociocultural characteristics of the users to the information collected by the professionals during the psychological pathways until human satisfaction was achieved. We provide a retrospective description of an experience that took place between April and June 2020, and which involved users of the maternal-infant area calling from the whole Italy. (1) Methods: The aims of this study were to investigate the configuration of the indicators identified and to detect the possible correlations between them in the sample. There were 193 users who took advantage of the Service, 160 of whom continued beyond the reception service; it is this group that we report the findings from here. The tool used was a form reporting access to care and interventions, and the resulting data underwent a content analysis and the indicators were subject to non-parametric statistical analysis to analyze differences and relationships. (2) Results: There were many correlations among the indicators that revealed a high prevalence of calls due to personal motivations and requests for support, which later allowed users to gain a greater understanding of the underlying problems they were facing. The professionals running the service noticed a prevalence of weaknesses attributable to the negative emotions of its users, alongside a presence of cognitive and relational resources. The professionals' interventions, which can be characterized by a prevalence of social support, psychological rehabilitation, and psychoeducation, achieved outcomes of redefining users' relationships with themselves and others. All of the service's users have expressed a high level of satisfaction with it. (3) Discussion: Our results revealed the protective and transformative effects of the service for its users and the underlying importance of having an easily accessible psychological support system in place during emergencies, like the recent pandemic. In conditions like these, the great value of a remote support service should be noted, and despite its limitations, assures its own efficacy when a medical emergency precludes closer in-person forms of psychological assistance. [ABSTRACT FROM AUTHOR]

The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Patients with haemoglobin disorders, particularly β-thalassaemia or sickle cell disease (SCD) or combined forms, on account of their underlying disease pathology and associated (iron load mainly in the case of thalassaemia) co-morbidities are defined as high-risk individuals prone to develop more severe complications from coronavirus disease-2019 (COVID-19). Despite the fact that epidemiological evidence concerning severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection in these patients is currently limited across the world, it is expected that COVID-19 pandemic will have a very serious, negative impact on national economies, healthcare and social systems and consequently significant respective repercussions on the patients particularly chronic ones, and their families. Although this may be a temporary challenge in some countries of high HDI and robust health, public health and social infrastructures, this can be a long term challenge with serious to tragic consequences in countries particularly devoid of universally covered heath care systems. Thalassaemia International Federation (TIF) in this present paper summarises the key challenges as expressed by the patients, their families and involved health care professionals themselves prior and consequent to COVID-19 pandemic, describes its response during the pandemic and expresses its position in support of its global patient community. [ABSTRACT FROM AUTHOR]

In comparison with other respiratory viruses, the current COVID-19 pandemic's rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash bank notes. SARS-CoV-2 remains on cash paper money for period around four times larger than influenza A virus and is absorbed by cash notes two and a half times more effectively than influenza A (our model). During the pandemic, cryptocurrencies have gained attractiveness as an "epidemiologically safe" means of transactions. On the basis of the authors' gallop polls performed online with social networks users in 44 countries in 2020–2021 (the total number of clear responses after the set repair 32,115), around 14.7% of surveyed participants engaged in cryptocurrency-based transactions during the pandemic. This may be one of the reasons of significant rise of cryptocurrencies rates since mid-March 2020 till the end of 2021. The paper discusses the reasons for cryptocurrency attractiveness during the COVID-19 pandemic. Among them, there are fear of SARS-CoV-2 spread via cash contacts and the ability of the general population to mine cryptocurrencies. The article also provides a breakdown of the polled audience profile to determine the nationalities that have maximal level of trust to saving and transacting money as cryptocurrencies. [ABSTRACT FROM AUTHOR]

The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pneumonia epidemic has had an enormous impact on people's lives, particularly aspects of life such as consumption, and has therefore brought new elements to the expansion of Consumer behavior theory. Methods: This paper searches the literature on consumption research conducted from 1981 to 2021, including sources such as CNKI, Wanfang, Google Scholar, and Web of Science. Through the exploration of the existing relevant literature, this article found that the COVID-19 pneumonia epidemic has had a profound impact on consumption willingness, consumption patterns, and consumption objects, and, as such, has newly expanded the theoretical model of consumer behavior. Results: Through reviewing the literature, this paper found some results. For example with regard to consumption patterns, early studies and the impact of COVID-19 was focused on online consumption, however in the context of COVID-19, scholars proposed paying attention to the combination of online and offline development. Conclusion: The COVID-19 pneumonia epidemic has had a profound effect on consumer behavior worldwide. Under the current economic depression, the government should take adequate measures in order to respond to the new changes in consumer behavior and therefore promote economic growth. For example, the government should encourage the combination of online and offline business operation modes to break the boundaries of customer groups and supply chains, so that consumers can buy anytime and anywhere. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies. [ABSTRACT FROM AUTHOR]

This thought experiment in comparison ponders a Black man's conviction that his Hebrew identity would make him immune to COVID-19. Surfacing the history of the claims and the scholar's own suspicions, the paper examines the layered politics of identification. Contra an essentialist understanding of the terms, "Hebrew" and "Hebrews" are shown to be classificatory events, ones imbricated in the dynamics of racecraft. Furthermore, a contextualization of the "race religion" model of 19th century scholarship, 20th century US religio-racial movements, and the complicated legacy of Tuskegee in 21st century Black vaccine hesitancy help to outline the need for inquisitiveness rather than hubris in matters of comparison. In so doing, this working paper advances a model of the public scholar as a questioner of categories and a diagnostician of classification. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of COVID-19's vulnerability context on Malawi's Energy Sector and outlines mechanisms for enhancing Malawi's energy-sector resilience based on experiences from a range of stakeholders. The investigation was conducted online by inviting purposively selected stakeholders to create presentations responding to thematic questions. The final sample had 19 stakeholders with representation from policy-makers, regulatory bodies, national grid supply players, off-grid players, development agencies, bankers, professional bodies, civil society, and women's rights bodies. The presentations from the stakeholders highlighted how COVID-19 affects the operation costs of energy systems and implementation of energy systems projects in areas that require stimulus packages to contain energy system delivery costs and prevent disruption of essential services amid the COVID-19 pandemic. These services include stakeholder responses to COVID-19 in the energy sector, the role of digital payments particularly when purchasing electricity units, and the state of third-party service providers such as banks and mobile network operators to enhance preparedness and continuity of operations for the energy sector. Based on the findings in these thematic areas and an application of systems thinking in the analysis, the paper finally makes recommendations on how Malawi and similar low-income countries can strategise to enhance energy systems resilience. [ABSTRACT FROM AUTHOR]

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. A general econometric panel model is applied to investigate the relationship between COVID-19 infection and death announcements with oil price volatility. The paper uses data from six geographical zones, Europe, Africa, Asia, North America, South America, and Oceania for the period 21 January 2020 until 13 May 2021 and the empirical findings show that COVID-19 deaths affected oil volatility significantly. This conclusion is confirmed by a second stage analysis applied separately for each geographical area. The only geographical area where the existence of correlation is not confirmed between the rate of increase in deaths and the volatility of the price of crude oil is Asia. The conclusions of this study clearly suggest that COVID-19 is a new risk component on top of economic and market uncertainty that affects oil prices and volatility. Overall, our results are useful for policy-makers, especially in the case of a new wave of infection and deaths in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: The human–animal bond is a powerful agent for reducing feelings of loneliness and social isolation; however, due to the COVID-19 pandemic, our typical avenues for accessing human–animal interaction (HAI) have been drastically impacted. Because the way we interact with animals has shifted during these times of collective struggle, we have ultimately grown in our appreciation of the human–animal bond. This paper will outline the impact of the pandemic on our relationships with animals, while also discussing the ways in which organizations that facilitate HAI have responded to the pandemic. Preparations for post-pandemic re-entry into the community with our animals will be outlined, and suggestions for future research and best practices on this topic will be provided. Access to human–animal interactions (HAI) have been influenced by the COVID-19 pandemic. Service animals that were trained and accustomed to daily access to public places had to adjust to staying at home. Therapy animals and their handlers who previously visited with many of the populations most vulnerable to the virus have had to halt their programming. Professionals who utilize animal-assisted interventions (AAI) have had to develop new strategies for providing goal-oriented care. Even the landscape for companion animals has been significantly altered, leading to behavioral changes and new practices for pet owners and veterinarians. While animals and their human companions face new challenges, our recognition of the power of the human–animal bond (HAB) has grown, as it provides a vital need for connection during this time of isolation. In this paper, we will not only focus on describing the new status quo related to various kinds of animals and the public's access to HAI, but will also offer suggestions for sharing the human–animal bond during a time in which physical connections are limited. Organizational insights from the service and therapy animal fields will be explored, and findings related to the auspiciousness of new initiatives, such as animal-related engagement (ARE), will be presented. Recommendations for people who share their lives with any of these kinds of animals will be made to ensure both human and animal welfare. Finally, future research and best practices will be suggested, so we can empirically understand and develop these revised offerings to ultimately bring HAI to a wider audience than ever before. [ABSTRACT FROM AUTHOR]

Retail trade is one of many links of the food supply chain to the consumer. Therefore, the efficiency of its operation is influenced by the earlier located links in the chain, first of all by agriculture, processing, logistics, and wholesale trade, as well as by consumers. The research problem in this paper, specified in the form of questions, is as follows: Has the 2020 pandemic reduced the performance of food retailing functions in less-developed regions of Poland? What is the assessment of the degree of their implementation? What directions should be taken at the micro and macro level to increase resilience to the negative effects of future crisis phenomena, in the implementation of the functions of this trade? The cognitive aim of the study was to examine the degree of performance of economic and social functions by the food retail trade in Poland, after one year of the SARS-CoV-2 pandemic (with the example of the Lubuskie region). For its implementation, four research hypotheses were formulated. The work was carried out in three stages, theoretical-interpretative, methodological, empirical, and consisted of three parts. In the experimental part, the method of CATI interviews with store owners/managers and the method of advanced statistics, classification, and visualization were used. The article brings new cognitive and normative value concerning food retailing in Poland. The results of the study enabled the realization of the aim of the paper and the verification of the hypotheses. The conclusions are presented in the summary of the individual parts of the empirical data analysis and in the conclusion of the paper. The paper contributes to the literature a new recognition of the degree of performance of the seven examined functions by food retail units, during the pandemic. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong's test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. [ABSTRACT FROM AUTHOR]

The property sector is revitalised to incorporate sustainability, specifically the biophilic design, to encourage human interaction with nature. Thus, there is an urgent need to communicate the biophilic design elements amongst stakeholders, especially the foreign workers, to ensure the project meets the requirement. However, standard project communications management is no longer practical, as the construction industry worldwide has been severely impacted by the Coronavirus Disease 2019 (COVID-19) pandemic. Hence, this paper evaluates the biophilic design elements to be communicated and examines the project communications management breakdown relating to the biophilic design elements in the Malaysian construction industry during the COVID-19 pandemic. Through a systematic literature review (SLR), focus group discussion (FGD) and questionnaire survey on 147 foreign workers, this paper found that from 33 biophilic design element items, only 1 item is categorised as highly critical, whereas another 20 items are categorised as critical. In addition, 43 potential communications management breakdown items from a total of 66 items fall under the critical category. The finding suggests that communication breakdown is caused by the language barrier amongst foreign workers from different ethnicities as well as between foreign workers and the supervisors, since face to face communication is limited during the pandemic. [ABSTRACT FROM AUTHOR]