Face mask is an efficient tool to fight the Covid-19 pandemic and some factors increase the probability of its adoption.

This study examines the dynamic impact of face mask use on both infected cases and fatalities at a global scale by using a rich set of panel data econometrics. An increase of 100% of the proportion of people declaring wearing a mask (multiply by two) over the studied period lead to a reduction of around 12 and 13.5% of the number of Covid-19 infected cases (per capita) after 7 and 14 days respectively. The delay of action varies from around 7 days to 28 days concerning infected cases but is more longer concerning fatalities. Our results hold when using the rigorous controlling approach. We also document the increasing adoption of mask use over time and the drivers of mask adoption. In addition, population density and pollution levels are significant determinants of heterogeneity regarding mask adoption across countries, while altruism, trust in government and demographics are not. However, individualism index is negatively correlated with mask adoption. Finally, strict government policies against Covid-19 have a strong significant effect on mask use.

Social and moral psychology of COVID-19 across 69 countries.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty.

This paper analyses the directional spillover effects and connectedness for both return and volatility of nine US dollar exchange rates of globally most traded currencies under the influence of trade policy uncertainty. We find two interesting results over the study period ranging from December 1993 to July 2019. First, there exists asymmetric spillovers and connectedness among the considered exchange rates when trade policy uncertainty is present. Second, the volatility spillover is stronger than the return connectedness between exchange rate and trade policy uncertainty. These findings are robust to the presence of economic policy uncertainty effects. Concomitantly, the trade policy uncertainty patterns are also found to be useful for predicting currency market dynamics. Our findings contribute to the debate on the impact of trade policy uncertainty on the global economy and financial sector.

Trust and the stock market reaction to lockdown and reopening announcements: A cross-country evidence.

We conduct a country-level analysis with a sample of 44 countries to examine whether generalised social trust has an impact on the stock market reaction to government announcements of lockdown and reopening during the COVID-19 pandemic. We first conduct an event study to measure the global stock markets' reaction to government announcements of lockdown and reopening, which is measured by each stock market's cumulative abnormal return. We then employ regression analysis to investigate the relationship between generalised social trust and the stock markets' reaction to government announcements of lockdown and reopening. The results show that government announcement of lockdown had a significant negative influence on most of the stock markets, whereas the magnitude of the stock markets' reaction to government announcement of reopening is relatively marginal, indicating a possible negative bias. Moreover, we find that generalised social trust is positively related to the stock markets' reaction to government announcement of lockdown and negatively related to the stock markets' reaction to government announcement of reopening.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning.

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

How education and GDP drive the COVID-19 vaccination campaign.

BACKGROUND: Since vaccination is the decisive factor for controlling the COVID-19 pandemic, it is important to understand the process of vaccination success which is not well understood on a global level. The study is the first to judge the now completed "first wave" of the vaccination efforts. The analysis is very relevant for the understanding why and where the vaccination process observed got stuck by the end of 2021. METHODS: Using data from 118 countries globally and weighted least squared and survival analysis, we identify a variety of factors playing crucial roles, including the availability of vaccines, pandemic pressures, economic strength measured by Gross Domestic Product (GDP), educational development, and political regimes. RESULTS: Examining the speed of vaccinations across countries until the Fall of 2021 when the global process got stuck, we find that initially authoritarian countries are slow in the vaccination process, while education is most relevant for scaling up the campaign, and the economic strength of the economies drives them to higher vaccination rates. In comparison to North and Middle America, European and Asian countries vaccinated initially fast for 5% and 10% vaccination rate thresholds, but became rather slow reaching the 30% vaccination level and above. The findings are robust to various applied estimation methods and model specifications. CONCLUSIONS: Democratic countries are much faster than authoritarian countries in their vaccination campaigns when controlling for other factors. This finding suggests that the quality of government and the political environment play a key role in popular support for government policies and programs. However, despite the early success of their vaccination campaigns, the democratic country group has been confronted with strong concerns of vaccine reluctance among their vast populations, indicating the two most potent variables explaining the speed of the COVID-19 vaccination campaign are education and economic conditions.

Tail risk connectedness in clean energy and oil financial market.

This research investigates the connectedness and the tail risk spillover between clean energy and oil firms, from January 2011 to October 2021. To this, we use the Tail-Event driven NETworks (TENET) risk model. This approach allows for a measurement of the dynamics of tail-risk spillover for each sector and firm. Hence, we can provide a detailed picture of the existing extreme relationships within these markets. We find that the total connection between the markets varies during the period analysed, showing how the uncertainty in oil price plays a critical role in the risk dynamics for oil companies. Also, we find that relationships between energy firms tend to be intrasectoral; that is, each sector receives (emits) risk from (to) itself. These results can have important practical implications for risk management and policymakers.

The race Against Time to Save Human Lives During the COVID-19 With Vaccines: Global Evidence.

Voluminous vaccine campaigns have been used globally, since the COVID-19 pandemic has brought devastating mortality and destructively unprecedented consequences to different aspects of economies. This study aimed to identify how the numbers of new deaths and new cases per million changed after half of the population had been vaccinated. This paper used actual pandemic consequence variables (death and infected rates) together with vaccination uptake rates from 127 countries to shed new light on the efficacy of COVID-19 vaccines. The 50% uptake rate was chosen as the threshold to estimate the real benefits of vaccination campaigns for reducing COVID-19 infection and death cases using the difference-in-differences (DiD) imputation estimator. In addition, a number of control variables, such as government interventions and people's mobility patterns during the pandemic, were also included in the study. The number of new deaths per million significantly decreased after half of the population was vaccinated, but the number of new cases did not change significantly. We found that the effects were more pronounced in Europe and North America than in other continents. Our results remain robust after using other proxies and testing the sensitivity of the vaccinated proportion. We show the causal evidence of significantly lower death rates in countries where half of the population is vaccinated globally. This paper expresses the importance of vaccine campaigns in saving human lives during the COVID-19 pandemic, and its results can be used to communicate the benefits of vaccines and to fight vaccine hesitancy.

COVID-19 challenges and firm responses: Analysis of a city-wide census in a developing country.

We use the unique data set of 16,300 firms' responses for the large-scale census survey in an urban area at Ho Chi Minh City (Vietnam) to study how firms perceived their problems and responded to during the COVID-19 pandemic. We provide estimates of 65 cross-associations between a firm's challenges during the pandemic and their responses. We find several firm characteristics that suggest increased likelihood of pandemic response, including some, such as state ownership, which are typically associated with being slow to respond to market conditions. A theoretical model is posited that matches with the rapidly declared survey response.

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

Automated liquid handling robot for rapid lateral flow assay development.

The lateral flow assay (LFA) is one of the most popular technologies on the point-of-care diagnostics market due to its low cost and ease of use, with applications ranging from pregnancy to environmental toxins to infectious disease. While the use of these tests is relatively straightforward, significant development time and effort are required to create tests that are both sensitive and specific. Workflows to guide the LFA development process exist but moving from target selection to an LFA that is ready for field testing can be labor intensive, resource heavy, and time consuming. To reduce the cost and the duration of the LFA development process, we introduce a novel development platform centered on the flexibility, speed, and throughput of an automated robotic liquid handling system. The system comprises LFA-specific hardware and software that enable large optimization experiments with discrete and continuous variables such as antibody pair selection or reagent concentration. Initial validation of the platform was demonstrated during development of a malaria LFA but was readily expanded to encompass development of SARS-CoV-2 and Mycobacterium tuberculosis LFAs. The validity of the platform, where optimization experiments are run directly on LFAs rather than in solution, was based on a direct comparison between the robotic system and a more traditional ELISA-like method. By minimizing hands-on time, maximizing experiment size, and enabling improved reproducibility, the robotic system improved the quality and quantity of LFA assay development efforts.

National identity predicts public health support during a global pandemic.

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.

What Drives Bitcoin? An Approach from Continuous Local Transfer Entropy and Deep Learning Classification Models.

Bitcoin has attracted attention from different market participants due to unpredictable price patterns. Sometimes, the price has exhibited big jumps. Bitcoin prices have also had extreme, unexpected crashes. We test the predictive power of a wide range of determinants on bitcoins' price direction under the continuous transfer entropy approach as a feature selection criterion. Accordingly, the statistically significant assets in the sense of permutation test on the nearest neighbour estimation of local transfer entropy are used as features or explanatory variables in a deep learning classification model to predict the price direction of bitcoin. The proposed variable selection do not find significative the explanatory power of NASDAQ and Tesla. Under different scenarios and metrics, the best results are obtained using the significant drivers during the pandemic as validation. In the test, the accuracy increased in the post-pandemic scenario of July 2020 to January 2021 without drivers. In other words, our results indicate that in times of high volatility, Bitcoin seems to self-regulate and does not need additional drivers to improve the accuracy of the price direction.

The impact of COVID-19 on the stock market crash risk in China.

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.

Feverish sentiment and global equity markets during the COVID-19 pandemic.

This paper proposes a new approach to estimating investor sentiments and their implications for the global financial markets. Contextualising the COVID-19 pandemic, we draw on the six behavioural indicators (media coverage, fake news, panic, sentiment, media hype and infodemic) of the 17 largest economies and data from 1 st January 2020 to 3 rd February 2021. Our key findings, obtained using a time-varying parameter-vector auto-regression (TVP-VAR) model, indicate the total and net connectedness for the new index, entitled 'feverish sentiment'. This index provides us insight into economies that send or receive the sentiment shocks. The construction of the network structures indicates that the United Kingdom, China, the United States and Germany became the epicentres of the sentimental shocks that were transmitted to other economies. Furthermore, we also explore the predictive power of the newly constructed index on stock returns and volatility. It turns out that investor sentiment positively (negatively) predicts the stock volatility (return) at the onset of COVID-19. This is the first study of its kind to assess international feverish sentiments by proposing a novel approach and its impacts on the equity market. Based on empirical findings, the study also offers some policy directions to mitigate the fear and panic during the pandemic.

Critical care ultrasound in geriatric trauma resuscitation leads to decreased fluid administration and ventilator days.

BACKGROUND: Geriatric trauma populations respond differently than younger trauma populations. Critical care ultrasound (CCUS) can guide resuscitation, and it has been shown to decrease intravenous fluid (IVF), lower time until operation, and lower mortality in trauma. Critical care ultrasound-guided resuscitation has not yet been studied in geriatric trauma. We hypothesized that incorporation of CCUS would decrease amount of IVF administered, decrease time to initiation of vasopressors, and decrease end organ dysfunction. METHODS: A PRE-CCUS geriatric trauma group between January 2015 and October 2016 was resuscitated per standard practice. A POST-CCUS group between January 2017 and December 2018 was resuscitated based on CCUS performed by trained intensivist upon admission to the intensive care unit and 6 hours after initial ultrasound. The PRE-CCUS and POST-CCUS groups underwent propensity score matching, yielding 60 enrollees in each arm. Retrospective review was conducted for demographics, clinical outcomes, and primary endpoints, including amount of IVF in the first 48 hours, duration to initiation of vasopressor use, and end organ dysfunction. Wilcoxon two-sample, χ2 tests, and κ statistics were performed to check associations between groups. RESULTS: There was no statistical difference between PRE-CCUS and POST-CCUS demographics and Injury Severity Scores. Intravenous fluid within 48 hours decreased from median [interquartile range] of 4941 mL [4019 mL] in the PRE-CCUS to 2633 mL [3671 mL] in the POST-CCUS (p = 0.0003). There was no significant difference between the two groups in time to initiation of vasopressors, vasopressor duration, lactate clearance, intensive care unit length of stay, or hospital length of stay. There was a significant decrease in ventilator days, with 26.7% PRE-CCUS with ventilation longer than 2 days, and only 6.7% POST-CCUS requiring ventilation longer than 2 days (p = 0.0033). CONCLUSION: Critical care ultrasound can be a useful addition to geriatric resuscitation. The POST-CCUS received less IV fluid and had decreased ventilator days. While mortality, lactate clearance, complications, and hospital stay were not statistically different, there was a perception that CCUS was a useful adjunct for assessing volume status and cardiac function. LEVEL OF EVIDENCE: Therapeutic, level II.

The nexus between black and digital gold: evidence from US markets.

In the context of the debate on cryptocurrencies as the 'digital gold', this study explores the nexus between the Bitcoin and US oil returns by employing a rich set of parametric and non-parametric approaches. We examine the dependence structure of the US oil market and Bitcoin through Clayton copulas, normal copulas, and Gumbel copulas. Copulas help us to test the volatility of these dependence structures through left-tailed, right-tailed or normal distributions. We collected daily data from 5 February 2014 to 24 January 2019 on Bitcoin prices and oil prices. The data on bitcoin prices were extracted from coinmarketcap.com. The US oil prices were collected from the Federal Reserve Economic Data source. Maximum pseudo-likelihood estimation was applied to the dataset and showed that the US oil returns and Bitcoin are highly vulnerable to tail risks. The multiplier bootstrap-based goodness-of-fit test as well as Kendal plots also suggest left-tail dependence, and this adds to the robustness of the results. The stationary bootstrap test for the partial cross-quantilogram indicates which quantile in the left tail has a statistically significant relationship between Bitcoin and US oil returns. The study has crucial implications in terms of portfolio diversification using cryptocurrencies and oil-based hedging instruments.

Carbon emissions determinants and forecasting: Evidence from G6 countries.

We examine the explanatory and forecasting power of economic growth, financial development, trade openness and FDI for CO2 emissions in major developed economies within the context of the debate on curbing CO2 emissions Post-Paris Agreement (COP21). Using data from G-6 countries from 1978 to 2014 and employing a set of empirical approaches, we find weak evidence of the Environmental Kuznets Curve, while economic growth, capital market expansion, and trade openness are found to be major drivers of carbon emissions. Carbon emissions are also weakly and negatively affected by stock market capitalization and FDI. Moreover, the forecasting performance is quite good, particularly by augmenting the model with energy consumption and oil prices. With respect to climate commitments, our empirical findings reveal important policy implications.

Prognosticating Outcomes and Nudging Decisions with Electronic Records in the Intensive Care Unit Trial Protocol.

Expert recommendations to discuss prognosis and offer palliative options for critically ill patients at high risk of death are variably heeded by intensive care unit (ICU) clinicians. How to best promote such communication to avoid potentially unwanted aggressive care is unknown. The PONDER-ICU (Prognosticating Outcomes and Nudging Decisions with Electronic Records in the ICU) study is a 33-month pragmatic, stepped-wedge cluster randomized trial testing the effectiveness of two electronic health record (EHR) interventions designed to increase ICU clinicians' engagement of critically ill patients at high risk of death and their caregivers in discussions about all treatment options, including care focused on comfort. We hypothesize that the quality of care and patient-centered outcomes can be improved by requiring ICU clinicians to document a functional prognostic estimate (intervention A) and/or to provide justification if they have not offered patients the option of comfort-focused care (intervention B). The trial enrolls all adult patients admitted to 17 ICUs in 10 hospitals in North Carolina with a preexisting life-limiting illness and acute respiratory failure requiring continuous mechanical ventilation for at least 48 hours. Eligibility is determined using a validated algorithm in the EHR. The sequence in which hospitals transition from usual care (control), to intervention A or B and then to combined interventions A + B, is randomly assigned. The primary outcome is hospital length of stay. Secondary outcomes include other clinical outcomes, palliative care process measures, and nurse-assessed quality of dying and death.Clinical trial registered with clinicaltrials.gov (NCT03139838).