ABSTRACT
Due to its impact on all human activity, the new virus has therefore aroused increased interest in research. A large number of medical papers related to COVID-19 have been published as a result of the scientific community response to this emerging infectious disease. All measures that combat the spread of the disease came at a cost and the people had to add one more hardship which contributed to the rise in social inequality. Moreover, the costs for internet access, proper equipment to support online education, private healthcare, facemasks, and disinfectants have become a burden for a segment of Romanian society. In this study, 18 Romanian universities a Medicine bachelor specialisation, both public and private, were analysed to determine the dataset query from Web of Science. Our research aimed to assess the impact of COVID-19 papers written by authors affiliated with Romanian universities with a medicine specialisation, which are the factors that influence the likelihood of a paper being cited and how the number of citations varies by university type. The obtained results reveal that University of Medicine and Pharmacy "Carol Davila" from Bucharest records the highest number of published papers, while "APOL-LONIA" University of Ia.i has the highest number of citations in both WOS Core Collection and in all WOS Databases per research. Furthermore, there is a significant difference in the number of citations between papers with and without a PUB MED ID. Logistic models show that neither the type of university nor the property type of the university (public or private) are determinants of the likelihood of an article being cited in the WOS core collection or in all WOS databases. As a consequence, a research paper with PUB MED ID and a higher number of pages, has a higher chance to be cited.
ABSTRACT
The COVID-19 pandemic represents one of the most severe challenges in the recent history of public health. The aim of this study is to estimate the transmission rate parameter (beta) and to predict the epidemic progression in Spain. We integrated data from Our World in Data. Our model considered a mean time from infection to death to be 24 days and the results of the seroprevalence survey in Spain. We calculated beta using a SEIR model estimated by least squares. We also used a SEIR model to evaluate four scenarios: 1) model 1: no containment measures, 2) model 2: containment measures from the beginning of the epidemic, 3) model 3: mild measures since the 20th day, 4) model 4: strict containment measures since the 20th day. The estimated beta parameter was 1.087. We calculated 41,210,330 infected people and 725,302 deaths in model 1;165,036 infected people and 2,905 deaths in model 2;4,640,400 infected people and 81,671 deaths in model 3;and, 62.012 infected people and 1,091 deaths in model 4. Peak of the epidemic varied from 69th day in model 1 to 216th day in model 4. Containment measures prevented a scenario with a significant increase in deaths and infected people. Our findings showed that, by stricter interventions such as quarantine and isolation could lead to reduce the potential peak number of COVID-19 cases and delay the time of peak infection.
ABSTRACT
Women are facing deep and lasting inequalities on the labor market. The effect of the pandemic on women's employment is evident after two years of pandemic, it has significantly affected the level of employment and increased the risk of women at work, inequalities have deepened and the recovery period will be longer than for men. In this paper we present an econometric analysis of occupational gender disparities at European level, considering as predictors the educational level and the countries' development level. We inspected the influence of pandemic on employment gender gap focusing on the gender occupational gap, gender disparities in education and whether the gender disparities in employment significantly differs depending on the countries' development level. Main findings refer to the statistically proved influence of the pandemic factors on employment gender gap, more significant for employees with tertiary education and in the countries with a higher level of development. The data used in the analysis is provided by EU official statistics.
ABSTRACT
This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007-2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An R package "BSCOV" is provided to implement the proposed algorithms. for this article are available online.
ABSTRACT
We estimate changes in the rates of five FBI Part 1 crimes during the 2020 spring COVID-19 pandemic lockdown period and the period after the killing of George Floyd through December 2020. We use weekly crime rate data from 28 of the 70 largest cities in the United States from January 2018 to December 2020. Homicide rates were higher throughout 2020, including during early 2020 prior to March lockdowns. Auto thefts increased significantly during the summer and remainder of 2020. In contrast, robbery and larceny significantly declined during all three post-pandemic periods. Point estimates of burglary rates pointed to a decline for all four periods of 2020, but only the pre-pandemic period was statistically significant. We construct a city-level openness index to examine whether the degree of openness just prior to and during the lockdowns was associated with changing crime rates. Larceny and robbery rates both had a positive and significant association with the openness index implying lockdown restrictions reduced offense rates whereas the other three crime types had no detectable association. While opportunity theory is a tempting post hoc explanation of some of these findings, no single crime theory provides a plausible explanation of all the results. Supplementary materials for this article are available online.
ABSTRACT
Several studies have estimated the effects of various nonpharmaceutical interventions on the COVID-19 pandemic using a 'reduced form' approach. In this paper, I show that many different SIR models can generate virtually identical dynamics of the number of reported cases during the early stages of the epidemic and lead to the same reduced form estimates. In some of these models, policy interventions effectively reduce the transmission rate;in others, the growth of the reported number of cases slows down even though policy has little or no effect on the transmission rate. Thus, the effect of policy cannot be uniquely determined based on the reduced form estimates. This result holds regardless of whether time series or panel data is used in reduced form estimation. I also demonstrate that the reduced form estimates of the policy effect based on panel data specifications with two-way fixed effects can have the wrong sign.
ABSTRACT
We propose a simple dynamic model for estimating the relative contagiousness of two virus variants. Maximum likelihood estimation and inference is conveniently invariant to variation in the total number of cases over the sample period and can be expressed as a logistic regression. We apply the model to Danish SARS-CoV-2 variant data. We estimate the reproduction numbers of Alpha and Delta to be larger than that of the ancestral variant by a factor of 1.51 [CI 95%: 1.50, 1.53] and 3.28 [CI 95%: 3.01, 3.58], respectively. In a predominately vaccinated population, we estimate Omicron to be 3.15 [CI 95%: 2.83, 3.50] times more infectious than Delta. Forecasting the proportion of an emerging virus variant is straight forward and we proceed to show how the effective reproduction number for a new variant can be estimated without contemporary sequencing results. This is useful for assessing the state of the pandemic in real time as we illustrate empirically with the inferred effective reproduction number for the Alpha variant.
ABSTRACT
We assess the treatment effect of juvenile stay-at-home orders (JSAHO) on reducing the rate of SARS-CoV-2 infection spread in Saline County ("Saline"), Arkansas, by examining the difference between Saline's and control Arkansas counties' changes in daily and mean log infection rates of pretreatment (March 28-April 5, 2020) and treatment periods (April 6-May 6, 2020). A synthetic control county is constructed based on the parallel-trends assumption, least-squares fitting on pretreatment and socio-demographic covariates, and elastic-net-based methods, from which the counterfactual outcome is predicted and the treatment effect is estimated using the difference-in-differences, the synthetic control, and the changes-in-changes methodologies. Both the daily and average treatment effects of JSAHO are shown to be significant. Despite its narrow scope and lack of enforcement for compliance, JSAHO reduced the rate of the infection spread in Saline. Supplementary materials for this article are available online.
ABSTRACT
It is well known that the conventional cumulative sum (CUSUM) test suffers from low power and large detection delay. In order to improve the power of the test, we propose two alternative statistics. The backward CUSUM detector considers the recursive residuals in reverse chronological order, whereas the stacked backward CUSUM detector sequentially cumulates a triangular array of backwardly cumulated residuals. A multivariate invariance principle for partial sums of recursive residuals is given, and the limiting distributions of the test statistics are derived under local alternatives. In the retrospective context, the local power of the tests is shown to be substantially higher than that of the conventional CUSUM test if a break occurs in the middle or at the end of the sample. When applied to monitoring schemes, the detection delay of the stacked backward CUSUM is found to be much shorter than that of the conventional monitoring CUSUM procedure. Furthermore, we propose an estimator of the break date based on the backward CUSUM detector and show that in monitoring exercises this estimator tends to outperform the usual maximum likelihood estimator. Finally, an application of the methodology to COVID-19 data is presented.
ABSTRACT
Digitization in the health sector is unequally distributed by activities and specializations, but it remains a trend that will change the employment model, with jobs disruption and infusion of financial capital and associated technologies. The facilities offered by digitalization not only offer solutions to adapt medical services to the challenges / restrictions of Covid-19 but also offer multiple possibilities to access expert services or reduce waiting times on the value chain of services, allowing to increase quality in perspective. patient-centered treatments. The aim of the research is to identify to what extent factors such as the level of economic development, the financing of the health sector and the external mobility of specialists influence the digital reform in the health sector. The results confirm the significant influence of the level of economic development and health spending on the potential for digitization of jobs in the health sector. It also highlights that e-health services have a reverse impact on the migration of doctors.
ABSTRACT
This paper illustrates how to handle a sequence of extreme observations-such as those recorded during the COVID-19 pandemic-when estimating a vector autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it may underestimate uncertainty.
ABSTRACT
Assessing the infection fatality rate (IFR) of SARS-CoV-2 in a population is a controversial issue. Due to asymptomatic courses of COVID-19, many infections remain undetected. Reported case fatality rates are therefore poor estimates of the IFR. We propose a strategy to estimate the IFR that combines official data on cases and fatalities with data from seroepidemiological studies in infection hotspots. The application of the method yields an estimate of the IFR of wild-type SARS-CoV-2 in Germany during the first wave of the pandemic of 0.83% (95% CI: [0.69%;0.98%]), notably higher than the estimate reported in the prominent study by Streeck et al. () (0.36% [0.17%;0.77%]) and closer to that obtained from a world-wide meta analysis (0.68% [0.53%;0.82%]), where the difference can be explained by Germany's disadvantageous age structure. Provided that suitable data are available, the proposed method can be applied to estimate the IFR of virus variants and other regions.
ABSTRACT
Humanity is experiencing intense and frequent changes caused by the COVID-19 pandemic, which increases uncertainty and complexity in decision-making. New challenges become urgent, mainly to issues related to the aging population, since the coronavirus affects the elderly more than adults. Finding alternatives to preserve the quality of life and work support of the elderly becomes a great challenge for society. In this context, science plays a vital role in proposing new solutions to solve the problems derived from this crisis, being this the primary motivation. The objectives of the article are to learn about the forgotten effects of the pandemic on economically active older adults and to indicate how fuzzy logic can help reduce risks by facilitating decision-making. The main contribution would be to correctly identify its causes and effects, such as the digital gap and job loss by this age group, and to point out corrective measures. The study's methodology is based on applied research, with a quantitative modeling and simulation approach through the Forgotten Effects Theory. The results allow us to predict and act more effectively on problems, seeking to increase the welfare, employability, and life expectancy of the elderly. The study highlights future lines of research on the subject.
ABSTRACT
We study the driving forces behind COVID-19 vaccination in German counties using a cross-sectional and a panel approach. We identify several factors that have a significant impact on vaccination rates. Our results are robust to different model specifications.
ABSTRACT
The 'BCG hypothesis' suggests that the Bacillus Calmette-Guerin (BCG) vaccine against tuberculosis limits the severity of COVID-19. We exploit the differential vaccination practices of East Germany and West Germany prior to reunification to test this hypothesis. Using a difference in regression discontinuities (RD-DD) design centred on the end of universal vaccination in the West, we find that differences in COVID-19 severity across cohorts in the East and West are insignificant or have the wrong sign. We document a sharp cross-sectional discontinuity in the severity of the disease, which we attribute to limited mobility across the long-gone border and which disappears when we control for social connectedness. Case and death data after the end of the first lockdown on 26 April does not display a discontinuity at the former border, suggesting that mobility (as opposed to BCG vaccination) played a major role during the initial outbreak.
ABSTRACT
South Korea publicly disclosed detailed location information of individuals who tested positive for COVID-19. We quantify the effect of public disclosure on the transmission of the virus and economic losses in Seoul. The change in commuting patterns due to public disclosure lowers the number of cases by 60,000 and the number of deaths by 2,000 in Seoul over two years. Compared to a city-wide lockdown that results in the same number of cases over two years as the disclosure scenario, the economic cost of such a lockdown is almost four times higher.
ABSTRACT
We study the impact of money on households during the COVID-19 pandemic. In March 2020, Colombia rolled out a new unconditional cash transfer (UCT) to 1 million households in poverty worth US$19 (PPP US$55.6) and paid every five to eight weeks. Using a randomized control trial and linked administrative and survey data, we find the UCT had positive (albeit modest) effects on measures of household well-being (e.g., financial health, food access). Moreover, the UCT boosted support for emergency assistance to households and firms during the crisis and promoted social cooperation. Finally, we explore the bottlenecks in expanding mobile money during a pandemic.
ABSTRACT
We use the high-frequency, decentralized implementation of stay-at-home (SAH) orders in the United States to disentangle the labor market effects of SAH orders from the general economic disruption wrought by the COVID-19 pandemic. We find that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9% of its employment level relative to other states. A back-of-the-envelope calculation implies that of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. We present a currency union model to provide conditions for mapping this estimate to aggregate employment losses.
ABSTRACT
We survey a representative sample of U.S. households to study how exposure to the COVID-19 stock market crash affects expectations and planned behavior. Wealth shocks are associated with upward adjustments of expectations about retirement age, desired working hours, and household debt but have only small effects on expected spending. We provide correlational and experimental evidence that beliefs about the duration of the stock market recovery shape households' expectations about their own wealth and their planned investment decisions and labor market activity. Our findings shed light on the implications of household exposure to stock market crashes for expectation formation.