Trump's COVID-19 tweets and Dr. Fauci's emails.

The paper features an analysis of former President Trump's early tweets on COVID-19 in the context of Dr. Fauci's recently revealed email trove. The tweets are analysed using various data mining techniques, including sentiment analysis. These techniques facilitate exploration of content and sentiments within the texts, and their potential implications for the national and international reaction to COVID-19. The data set or corpus includes 159 tweets on COVID-19 that are sourced from the Trump Twitter Archive, running from 24 January 2020 to 2 April 2020. In addition we use Zipf and Mandelbrot's power law to calibrate the extent to which they differ from normal language patterns. A context for the emails is provided by the recently revealed email trove of Dr. Fauci, obtained by Buzzfeed on 1 June 2021 obtained under the Freedom of Information Act.

Common Mental Disorders and Economic Uncertainty: Evidence from the COVID-19 Pandemic in the U.S.

Mental health disorders represent an enormous cost to society, are related to economic outcomes, and have increased markedly since the COVID-19 outbreak. Economic activity contracted dramatically on a global scale in 2020, representing the worst crisis since the Great Depression. This study used the COVID Impact Survey to provide insights on the interactions of mental illness and economic uncertainty during COVID-19. We used a probability-based panel survey, COVID Impact Survey, conducted in the U.S. over three waves in the period April-June 2020. The survey covered individual information on employment, economic and financial uncertainty, mental and physical health, as well as other demographic information. The prevalence of moderate mental distress was measured using a Psychological Distress Scale, a 5-item scale that is scored on a 4-point scale (total range: 0-15). The mental distress effect of employment, economic, and financial uncertainty, was assessed in a logit regression analysis conditioning for demographic and health information. It is found that employment, health coverage, social security, and food provision uncertainty are additional stressors for mental health. These economic factors work in addition to demographic effects, where groups who display increased risk for psychological distress include: women, Hispanics, and those in poor physical health. A decrease in employment and increases in economic uncertainty are associated with a doubling of common mental disorders. The population-representative survey evidence presented strongly suggests that economic policies which support employment (e.g., job keeping, job search support, stimulus spending) provide not only economic security but also constitute a major health intervention. Moving forward, the economic uncertainty effect ought to be reflected in community level intervention and prevention efforts, which should include strengthening economic support to reduce financial and economic strain.

COVID-19 restrictions and age-specific mental health-U.S. probability-based panel evidence.

Social distancing, self-isolation, quarantining, and lockdowns arising from the COVID-19 pandemic have been common restrictions as governments have attempted to limit the rapid virus transmission. In this study, we identified drivers of adverse mental and behavioral health during the COVID-19 pandemic and whether factors such as social isolation and various restrictions serve as additional stressors for different age groups. Univariate and multivariate regression analyses were conducted on a unique dataset based on a national probability-based survey dedicated to understanding the impact of COVID-19 in the U.S., which includes 19 questions on the individual impact of restrictions, bans, and closures. The analysis used a moderate distress scale built on five questions related to mental health for 3,646 respondents. The mental health of young adults (18-34 years old) was the most affected by restrictions, while that of older adults (>55 years old) was less affected. In addition, demographic and health characteristics associated with differences in mental health varied by age group. The findings in this analysis highlight the differential mental health needs of different age groups and point to the marked necessity for differentiated and targeted responses to the mental health effects of COVID-19 by age group.

Alternative Global Health Security Indexes for Risk Analysis of COVID-19.

Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of the SARS-CoV-2 virus that causes the COVID-19 disease, it is essential to enquire if an outbreak of the epidemic might have been anticipated, given the well-documented history of SARS and MERS, among other infectious diseases. If various issues directly related to health security risks could have been predicted accurately, public health and medical contingency plans might have been prepared and activated in advance of an epidemic such as COVID-19. This paper evaluates an important source of health security, the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly in an effective and timely manner. The GHS index numerical scores are calculated as the arithmetic (AM), geometric (GM), and harmonic (HM) means of six categories, where AM uses equal weights for each category. The GHS Index scores are regressed on the numerical score rankings of the six categories to check if the use of equal weights of 0.167 in the calculation of the GHS Index using AM is justified, with GM and HM providing a check of the robustness of the arithmetic mean. The highest weights are determined to be around 0.244-0.246, while the lowest weights are around 0.186-0.187 for AM. The ordinal GHS Index is regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal Global Health Security (GHS) Index, where the highest weight is 0.368, while the lowest is 0.142, so the estimated results are wider apart than for the numerical score rankings. Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, whereas Risk Environment and Prevention have the smallest effects. The quantitative and qualitative results are different when GM and HM are used.

Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19.

Environmental change created worldwide interest in investing in renewable energy. Less reliance on fossil fuels would have a substantial influence on investors for alternative energy, especially renewable energy. The literature has concentrated on empirical studies of herding behaviour in finance, but not in renewable energy. This paper fills the gap by investigating herding in renewable energy, using daily closing prices in renewable and fossil fuel energy stock returns in the USA, Europe, and Asia, for March 24, 2000-May 29, 2020, which covers the Global Financial Crisis (GFC) (2007-2009), the coronavirus crises of SARS (2003). And the ongoing COVID-19 (2019-2020) pandemic. The paper shows that: (1) for low extreme oil returns, investors are more likely to display herding in the stock market; (2) for SARS and COVID-19, herding is more likely during extremely high oil returns after the GFC; and (3) herding is more likely during periods of extremely low oil returns during the coronavirus crises. These results suggest that after the GFC, investors are more sensitive to asset losses, so they will be more likely to display herding in the stock market. However, during SARS and COVID-19, investors panic so they may unwisely sell their assets. There are strong cross-sector herding spillover effects from US fossil fuel energy to renewable energy, especially before the GFC, while the US fossil fuel energy market has a significant influence on the Europe and Asia renewable energy returns during COVID-19. During SARS, which was not a pandemic, US fossil fuels only had an impact on US renewable energy returns.

Modelling Economic Growth, Carbon Emissions, and Fossil Fuel Consumption in China: Cointegration and Multivariate Causality.

Most authors apply the Granger causality-VECM (vector error correction model), and Toda-Yamamoto procedures to investigate the relationships among fossil fuel consumption, CO2 emissions, and economic growth, though they ignore the group joint effects and nonlinear behaviour among the variables. In order to circumvent the limitations and bridge the gap in the literature, this paper combines cointegration and linear and nonlinear Granger causality in multivariate settings to investigate the long-run equilibrium, short-run impact, and dynamic causality relationships among economic growth, CO2 emissions, and fossil fuel consumption in China from 1965-2016. Using the combination of the newly developed econometric techniques, we obtain many novel empirical findings that are useful for policy makers. For example, cointegration and causality analysis imply that increasing CO2 emissions not only leads to immediate economic growth, but also future economic growth, both linearly and nonlinearly. In addition, the findings from cointegration and causality analysis in multivariate settings do not support the argument that reducing CO2 emissions and/or fossil fuel consumption does not lead to a slowdown in economic growth in China. The novel empirical findings are useful for policy makers in relation to fossil fuel consumption, CO2 emissions, and economic growth. Using the novel findings, governments can make better decisions regarding energy conservation and emission reductions policies without undermining the pace of economic growth in the long run.

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity.

This manuscript describes how to prepare fluidic biochips with Rainbow trout gill epithelial (RTgill-W1) cells for use in a field portable water toxicity sensor. A monolayer of RTgill-W1 cells forms on the sensing electrodes enclosed within the biochips. The biochips are then used for testing in a field portable electric cell-substrate impedance sensing (ECIS) device designed for rapid toxicity testing of drinking water. The manuscript further describes how to run a toxicity test using the prepared biochips. A control water sample and the test water sample are mixed with pre-measured powdered media and injected into separate channels of the biochip. Impedance readings from the sensing electrodes in each of the biochip channels are measured and compared by an automated statistical software program. The screen on the ECIS instrument will indicate either "Contamination Detected" or "No Contamination Detected" within an hour of sample injection. Advantages are ease of use and rapid response to a broad spectrum of inorganic and organic chemicals at concentrations that are relevant to human health concerns, as well as the long-term stability of stored biochips in a ready state for testing. Limitations are the requirement for cold storage of the biochips and limited sensitivity to cholinesterase-inhibiting pesticides. Applications for this toxicity detector are for rapid field-portable testing of drinking water supplies by Army Preventative Medicine personnel or for use at municipal water treatment facilities.

An econometric analysis of SARS and Avian Flu on international tourist arrivals to Asia.

This paper compares the impacts of SARS and human deaths arising from Avian Flu on international tourist arrivals to Asia. The effects of SARS and human deaths from Avian Flu are compared directly according to the number of human deaths. The nature of the short run and long run relationship is examined empirically by estimating a static line fixed effect model and a difference transformation dynamic model, respectively. Empirical results from the static fixed effect and difference transformation dynamic models are consistent, and indicate that both the short run and long run SARS effect have a more significant impact on international tourist arrivals than does Avian Flu. In addition, the effects of deaths arising from both SARS and Avian Flu suggest that SARS is more important to international tourist arrivals than is Avian Flu. Thus, while Avian Flu is here to stay, its effect is currently not as significant as that of SARS.

ARMAX modelling of international tourism demand.

Box-Jenkins (1970) models are often used to capture the autoregressive moving average of past observations of tourist arrivals from Japan to Taiwan and New Zealand. However, other explanatory variables, such as real income in the origin country, have also affected the demand for international travel. The purpose of this paper is to use the ARMAX model to investigate the dynamic relationship between tourism demand and real income of Japan, and to compare the findings with the single-equation model. Unit root tests and diagnostics are performed before estimating the income elasticity of travel demand by Japan for New Zealand and Taiwan based on seasonally unadjusted quarterly data for 1980(1) to 2004(2). The empirical results of the ARMAX model support the economic theory that the demand for international travel is positively related to income of the origin country.