Ecosystem degradation and the spread of Covid-19.

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

Uncovering the spatio-temporal impact of the COVID-19 pandemic on shared e-scooter usage: A spatial panel model.

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.

A Dynamic Interaction Semiparametric Function-on-Scalar Model

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects. We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions. In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic. The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies. Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data. In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations. For instance, we find that the interaction effect of the population aging and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic. The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model.

Does active transport lead to improved mood and performance? A panel study of travel changes during the Covid-19 lockdown in Norway

Everyday commuting is seen as a burden and an unwanted necessity for people. Recent studies have challenged this notion and have found that certain aspects of commuting can be positive. In particular, research has shown that active commuting can be an important source of everyday physical activity and a pause between arenas for daily routine. The current study uses the Covid-19 lockdown situation in Norway, and the associated travel restrictions, as a backdrop to study the relationship between active travel and self-reported mood and work performance. In a situation where people are strongly encouraged to take up active mobility forms in place of more passive forms, the often-encountered challenge of self-selection is reduced. A convenience sample was recruited via social media (N = 1319) in May 2020 and completed a total of six follow-up surveys over a period of four months, thus allowing for a panel design as well as a within-subjects comparison. The survey covered topics related to commute mode, experience of travel, current mood, and work performance. Background variables related to personality, general wellbeing as well as sociodemographic measures were also captured. Multivariate models show that those who during this period commute with active modes (walking and cycling) report a higher degree of travel satisfaction than users of passive modes (driving and public transport). Further, active modes are associated with being in a better mood, and with reporting higher work performance. Finally, looking at individuals who over time change travel mode (N = 151), we find that they report improved mood and work performance when travelling with active vs passive modes. The results have implications for policy makers and for employers looking for justification to spend company money on measures to increase active travel. © 2023 The Authors

On summary ROC curve for dichotomous diagnostic studies: an application to meta-analysis of COVID-19.

In a systematic review of a diagnostic performance, summarizing performance metrics is crucial. There are various summary models in the literature, and hence model selection becomes inevitable. However, most existing large-sample-based model selection approaches may not fit in a meta-analysis of diagnostic studies, typically having a rather small sample size. Researchers need to effectively determine the final model for further inference, which motivates this article to investigate existing methods and to suggest a more robust method for this need. We considered models covering several widely-used methods for bivariate summary of sensitivity and specificity. Simulation studies were conducted based on different number of studies and different population sensitivity and specificity. Then final models were selected using several existing criteria, and we compared the summary receiver operating characteristic (sROC) curves to the theoretical ROC curve given the generating model. Even though parametric likelihood-based criteria are often applied in practice for their asymptotic property, they fail to consistently choose appropriate models under the limited number of studies. When the number of studies is as small as 10 or 5, our suggestion is best in different scenarios. An example for summary ROC curves for chemiluminescence immunoassay (CLIA) used in COVID-19 diagnosis is also illustrated.

Multilevel IRT models for the analysis of satisfaction for distance learning during the Covid-19 pandemic.

The Covid-19 pandemic played a relevant role in the diffusion of distance learning alternatives to "traditional" learning based on classroom activities, to allow university students to continue attending lessons during the most severe phases of the pandemic. In such a context, investigating the students' perspective on distance learning provides useful information to stakeholders to improve effective educational strategies, which could be useful also after the end of the emergency to favor the digital transformation in the higher educational setting. Here we focus on the satisfaction in distance learning for Italian university students. We rely on data comprising students enrolled in various Italian universities, which were inquired about several aspects related to learning distance. We explicitly take into account the hierarchical nature of data (i.e., students nested in universities) and the latent nature of the variable of interest (i.e., students' learning satisfaction) through a multilevel Item Response Theory model with students' and universities' covariates. As the main results of our study, we find out that distance learning satisfaction of students: (i) depends on the University where they study; (ii) is affected by some students' socio-demographic characteristics, among which psychological factors related to Covid-19; (iii) is affected by some observable university characteristics.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

Determinants of Food Insecurity During COVID-19 Pandemic in Nigeria: A Random Effects Ordered Probit Approach

COVID-19 is reversing the global development progresses already attained over the past few decades. Attainment of zero hunger in the Sustainable Development Goals (SDGs) is being threatened and comprehensive approach in managing the pandemic is required. This paper analyzed the determinants of food insecurity status during COVID-19 pandemic in Nigeria. The data were the second, fourth and seventh rounds of COVID-19 National Longitudinal Phone Survey (NLPS) that were carried out in June, August and November 2020 respectively. Food insecurity status was computed with questions on Food Insecurity Experience Scale (FIES) and analyzed with Random Effects Ordered Probit Regression. The results showed that food security improved from 12.19% in June to 24.65% in November. There was a decline in the percentage of severely food insecure households in urban areas from 54.67% in June to 44.53% in November while that in rural areas declined from 60.69% in June to 45.34% in November. The Panel Probit regression showed presence of significant heterogeneity across the panels. Also, age, household size, male, tertiary education, North West residence, public administrator, business or traders and construction job showed statistical significance (p<0.05) with negative sign, while social assistance (p<0.05) has positive sign. Nonperception of COVID-19 risk based on morbidity tendency and financial impacts significantly reduced food insecurity (p<0.01). It was concluded that interventions to address COVID-19 pandemic must critically evaluate their welfare impacts given absence of effective social assistance and must take cognizance of households' vulnerability to hunger with focus on female headed households, illiterates, unemployed and youths.

THE FACTORS INFLUENCING GROSS DOMESTIC PRODUCT GROWTH IN THE POST-PANDEMIC PERIOD: THE CASE OF KOSOVO

This paper examined the factors that influenced the Gross Domestic Product growth (GDP) in the post-Covid-19 period in Kosovo. This paper explored the impact of consumption, remittances, exports, imports, and inflation on Kosovo's GDP growth using fixed effects regression analysis with data from various secondary sources to analyze their impact from Kosovo's perspective. The results demonstrated that consumption, remittances, and exports had a statistically significant influence on GDP growth during the post-pandemic economic lockdown stage, whereby imports and inflation had a little inverse relation. Further, the Hausman test statistics on the adequacy of the fixed-effect model selection represent a superior performance compared to the random effect model. The paper is the first that extensively explores the impact of these factors that drove GDP growth in the post-pandemic period in Kosovo's economy. The novelty of this paper is that it recognizes the response of governments to the pandemic and accurately identifies the macroeconomic factors that influenced GDP growth.

The impact of COVID-19 and working from home on the workspace retained at the main location office space and the future use of satellite offices.

There exists a substantial amount of research on the impact that the COVID-19 pandemic has had on significant changes in the location at which work takes place, especially working from home (WFH). There has been, however, very little systematic consideration given to the relationship between the substantial increase in WFH and the responses taken by organisations in reviewing their office (workspace) capacity needs in the future, including a switch of the mix of utilising workspace in the main office(s) and satellite office locations. The main aim of this paper is to explore the extent to which levels of working from home and increased use of rented satellite office space will be linked to changes in the amount of workspace required at the main office that was used pre-COVID-19. Using data from 459 businesses for three periods for pre-COVID-19, April 2022 (25 months after the outbreak of the pandemic) and stated intentions for 2023, we develop a random effects regression model for the Greater Sydney Metropolitan Area in which we identify some of the influences on the downsizing or not of the main office(s) work space, and comment on what we see as the most likely scenario for WFH and work space in the main office and rented satellite office space under the 'next normal'. The findings can be used to inform future commuting travel as well as changes in land use activity at specific locations, including possible reallocation of existing office space to other activity uses.

Improving wasting among children under-5 years in Malawi: The role of farm input subsidies.

Wasting among children under-5 years remains a public health problem in Malawi, despite the quest to improve food availability through Farm Input Subsidy Program (FISP). As such, the study examined the link between FISP and child wasting. Using Malawi Integrated Household Panel Surveys for 2013, 2016, and 2019, two-stage least squares approach was employed to run a Cobb Douglas production function and a correlated Random Effects (CRE) Model to account for endogeneity challenges and an unbalanced panel dataset. The study hypothesized the role of FISP to dietary diversity at the household level on child wasting [weight-for-height (WHZ)]. Based on the analysis, the study found that household access to FISP coupons was not a stand-alone predictor for low wasting among children under-5 years. However, increased maize production due to FISP coupon access significantly correlated with lower wasting likelihood incidences at the household level. Worth to note, that in 2015/16, households that had accessed FISP coupons and were in the central region had higher wasting probabilities among the children under-5 years in Malawi compared to other counterparts panels. This implies challenges to addressing wasting among children under-5 years- which can be attributed to higher redemption costs of the FISP coupon. Therefore, the current study suggests that input subsidies can improve the reduction of wasting among children under-5 years through specific pathways, among them, increased maize production and considering appropriate targeted approaches to ensure households access the inputs for sustained food availability, which in turn enhances improved the children under-5 years health dividends in Malawi.

Prevalence and outcomes of hyponatremia among COVID-19 patients: A systematic review and meta-analysis

Objectives: On March 2020, the WHO declared coronavirus disease 2019 (COVID-19) pandemic. COVID-19 is associated with various clinical syndromes, with electrolytes imbalances involved. This review aims to quantify the prevalence and outcomes of hyponatremia among COVID-19 patients, as well as to review the underlying pathophysiological mechanisms of hyponatremia among these patients. Methods: Using Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines, we conducted a systematic literature search using the electronic databases of Google Scholar, MEDLINE (PubMed), WHO Virtual Health Library, and ScienceDirect, without limitations regarding gender, geographical area, race or publication date, up until December 13, 2021. Primary outcomes measured were mortality, intensive care unit (ICU) admission, assisted ventilation need, and length of hospital stay (LOS). Secondary outcome was the mechanism underlying hyponatremia among COVID-19 patients. Results: From a total of 52 included studies, 23 underwent quantitative analysis. For the primary outcomes;proportions, odds ratios (OR), and standardized mean difference (SMD) were calculated using random effects model. The prevalence of hyponatremia was found to be 25.8%. Hyponatremia was found to be significantly associated with increased odds for mortality (OR = 1.97[95% CI, 1.50–2.59]), ICU admission (OR = 1.91 [95% CI, 1.56–2.35]), assisted ventilation need (OR = 2.04 [95% CI, 1.73– 2.38]), and with increased LOS (SMD of 5.74 h [95% CI, 0.092–0.385]). Regarding the mechanisms underlying hyponatremia, syndrome of inappropriate anti-diuretic hormone secretion (SIADH) was most commonly reported, followed by adrenal insufficiency, and finally hypovolemic hyponatremia due to gastrointestinal losses. Conclusion: Hyponatremia among COVID-19 patients is generally associated with poor outcomes, with SIADH being the most common underlying mechanism. [ FROM AUTHOR] Copyright of Journal of Health Sciences (Qassim University) is the property of Journal of Health Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The impact of financial development on environmental sustainability: A European perspective

This research aimed to analyze the impact of financial development on environmental sustainability. Data was collected for 34 countries in Europe, covering the period from 2000 to 2020. Data analysis was conducted using the Feasible Generalised Least Squares (FGLS) model, a random-effects model (specified by the Hausman test), and the Generalised Method of Moments (GMM) approach. It was found that lending rates are negatively related to CO2 emissions per capita, total CO2, and CO2 by the transport industry. It was also found that bank credit to the private sector increases total CO2 emissions and CO2 emissions from the power and transport industries. This study found that domestic credit to the private sector increases total CO2 emissions. An important implication of these results is that borrowers should be selected and monitored using more stringent criteria to ensure compliance with environmental requirements. This study has made multiple contributions. It has extended knowledge about how the financial sector impacts the environment. It has used two models that can handle issues of collinearity and heteroscedasticity. Its findings are useful for understanding the financial development-environmental health association in this unique COVID-19 pandemic context. © 2022 Elsevier Ltd

The effect of network structure on the purchase of virtual goods on social networking services

Purpose>This paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approach>To achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.Findings>The authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales;while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implications>The findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/value>This study provides implications in that it uses various and dynamic network structure variables with panel data.

Retail quality, market environment and business survival in the retail Apocalypse: an investigation of the sporting goods retail industry

Purpose>In the era of the retail Apocalypse, the surge of e-commerce has transmuted the competitive landscape for many traditional retailers that heavily rely on brick-and-mortar stores. This study examines the relationship among retail quality, market environment and businesses' survival in the context of the sporting goods retail industry.Design/methodology/approach>Based on a data set from yelp.com, the authors examine the survival of 1,360 stores within 306 zip codes in the United States using mixed effects logistic modeling.Findings>(1) Retail quality is positively related to survival, but the relationship is nonlinear;(2) the author find a null relationship between market competition and survival, which is subject to several competing interpretations;(3) 10% of the individual variation in survival is due to systematic differences between zip codes and (4) chain stores and stores with more heterogenous reviews have a higher closure rate.Originality/value>This study contributes to the literature by offering an empirical testing of the relationship between retail quality and business survival and examining the impact of trading area in the modern marketing milieu. The findings have practical implications for site selection and designing a service quality program.

What we have learned from two decades of epidemics and pandemics: A systematic review and meta-analysis of the psychological burden of frontline healthcare workers

Objective Given the ongoing COVID-19 pandemic and future disease threats, a profound comprehension of the impact of epidemics and pandemics on healthcare providers' mental health is needed to address these current and future global health challenges, Thus, aim of this study was to quantify psychological and psychosomatic symptoms among healthcare staff working on the frontlines of SARS-CoV-1, H1N1, Ebola, MERS, and SARS-CoV-2. Methods We systematically searched four databases and additional literature, including daily search alerts. Two reviewers independently performed all methodological steps, involving a third reviewer in case of dissent We pooled the overall symptom prevalence rates applying random effects modeling and calculated the 12 to account for statistical heterogeneity. Results Of 1995 retrieved records, we assessed 417 full texts for eligibility, and included 86 studies. Frontline providers suffered from various symptoms, like concern about transmitting the virus to family (60.39%, 95% CI 42.53-76.96) and concern about own health (45.97%, 95% CI 31.08-61.23), sleep disturbances (39.88%, 95% CI 27.70-52.72), burnout (31.81%, 95% CI 13.32-53.89), depression (25.72%. 95% CI 18.34-33.86), anxiety (25.36%, 95% CI 17.90-33.64), posttraumatic stress disorder (24.51%, 95% CI 18.16-31.46), and somatization (14.68%, 95% CI 10.67-19.18). Conclusion Our findings, based on information from 75,991 participants, highlight the severe psychological effects of epidemics and pandemics on frontline staff. As another COVID-19 wave, driven by Delta and, Omicron, puts even more strain on an already exhausted ' medical system and healthcare providers face aggression and threats from anti-vaccine activists, providing tailored psychological support for the healthcare workforce becomes all the more important.

A Spatiotemporal Analysis of the COVID-19 Pandemic in North Africa.

Spatial panel-data models are estimated to identify the factors of the prevalence of the coronavirus outbreak in North Africa. Using daily data on the number of cases collected between March 2020 and December 2021, three types of general models are investigated, and they include spatial spillovers between the neighboring countries of the region. In one model the spatial dependence is accounted for by adding a spatial lag of the dependent variable (SAR model). In an alternative specification, spatially correlated error terms are considered in the model (SEM), and in the third model a spatial lag dependent variable and spatially correlated errors are both added (SAC). To deal with unobservable individual heterogeneity, random and fixed individual effects specification are investigated in each of these models. The results of the maximum likelihood and generalized method of moments' estimations show that the lift of travel restrictions had an important impact on the spike in the numbers of COVID-19 cases in North Africa and that the effects of endogenous interactions between the countries are strongly significant. It is found that spatial spillovers and a change in the travel policy are the main factors that can explain the mechanism of spread the coronavirus pandemic in North Africa. However, more data on socio-demographic and behavioral variables and on vaccination rates are needed to better understand what caused the recent surge in the number of infections in the region.

The "Healthcare Workers' Wellbeing [Benessere Operatori]" Project: A Longitudinal Evaluation of Psychological Responses of Italian Healthcare Workers during the COVID-19 Pandemic.

BACKGROUND: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The "Healthcare workers' wellbeing (Benessere Operatori)" project aimed at identifying psychological and personal factors, influencing individuals' responses to the COVID-19 pandemic. METHODS: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. RESULTS: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers' mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. CONCLUSIONS: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Innovations and firm performance: a study of the global airline industry

The International Air Transport Association (IATA) reports that the impact of COVID-19 translates into a $630 billion reduction in tourism and travel related GDP and over 26 million job loss. This study examines how innovation influences company performance within the global airline industry covering the periods 2000 to 2020. Several fixed and random effect models and system-GMM approaches were employed. The findings indicate that higher investment in innovation helps increase profit margin and return on equity but reduces return on assets. Higher return from product development expenses (higher product development expenses) increases (decreases) profitability and efficiency of the company. Also, higher product development expense and profitability nexus indicates that companies may have to rely heavily on internal capital while financing for research and development (R&D). While national bailouts, mergers, climate change related sustainability agendas will be issues as airlines get back on slowly with the gradual opening of economies and vaccine miracle, the findings reported in this paper will bear significant policy implications for the sector overall.

Impacts of COVID-19 on the self-employed

This study estimates random effects and difference-in-difference-in-differences models to examine the initial impacts of COVID-19 on the employment and hours of unincorporated self-employed workers using monthly panel data from the Current Population Survey. For these workers, effects were visible in March as voluntary social distancing began, largest in April as complete shutdowns occurred, and slightly smaller in May as some restrictions were eased. We find differential effects by gender that favor men, by marital status and gender that favor married men over married women, and by gender, marital, and parental status that favor married fathers over married mothers. The evidence suggests that self-employed married mothers were forced out of the labor force to care for children presumably due to prescribed gender norms and the division and specialization of labor within households. Remote work and working in an essential industry mitigated some of the negative effects on employment and hours.Plain English SummaryAmong the unincorporated self-employed, married mothers were less likely to be employed and worked fewer hours during the COVID-19 pandemic than married fathers. Effects were visible in March as voluntary social distancing began, largest in April as complete shutdowns occurred, and slightly smaller in May as some restrictions were eased. Our results suggest that COVID-19 forced self-employed women back into the home due to gender norms about who cares for children. However, having a plausibly remote job or being in an essential industry helped mitigate some of the negative effects on employment and hours worked. Besides providing evidence that married mothers’ presence among the self-employed has been diminished by COVID-19, we find that the pandemic hurt the unincorporated self-employed more than other types of workers. This finding provides further evidence that it is important for researchers to distinguish between the incorporated and unincorporated self-employed when analyzing variation in self-employment at different points in the business cycle.