COVID-19 impact on digital companies' stock return: A dynamic data analysis.

The coronavirus crisis impact on the digital sector is undoubtedly an important issue that deserves to be studied. Researchers mostly focused on specific sectors such as tourism, healthcare sector, or the economy. This paper used a dynamic panel model to examine the COVID-19 crisis impact on the digital companiesfl stock return. The findings indicate that both of the monthly growth in total infected cases and total death cases caused by COVID-19 have significant positive effects on stock returns across digital companies. This novel results contradicts previous research findings and highlights that this crisis is slowing down all the economic sectors.

Assessing the spillover effects of various forms of energy on CO2 emissions - An empirical study based on dynamic spatial Durbin model.

Previous studies ignored the geospatial dynamics spillover effects of energy consumption on CO2 emissions while assessing such impacts in developed and developing countries. Moreover, most studies wrongfully assess spillover effects in its aggregated format rather than decomposing by its components. This is important as not all energy sources share the same characteristics. We fill these gaps in the literature by investigating the spillover effects of various forms of energy, including fossil fuels, renewable energy, and nuclear power, on CO2 emissions in 135 developed and developing countries from 2000 to 2019. We used the Dynamic Spatial Durbin Model (DSDM) to better understand the results. A series of indicative tests confirmed using the DSDM model and including spatial interaction of CO2 emissions in the analysis. Our findings show evidence of indirect spillover effects of the various energy sources on CO2 emissions. Further considering the spillover effects of the energy sources of neighbouring countries, the paper finds that the driving increase in CO2 emissions mainly came from the energy consumption of the country itself and neighbouring countries' energy consumption. Nevertheless, the results indicate that the direct effects of energy consumption often exceed its indirect effects. The results also confirm that total and fossil energy consumption harms the environment, whereas adopting renewable and nuclear energy sources reduces CO2 emissions. Lastly, we find nuclear energy is the most environmentally sustainable energy source. The study concludes that the Dynamic Spatial Durbin Model is paramount in estimating the environmental impact of energy consumption in our sample. The practical policy implications drawn from this study could be used to promote increased collaboration to hasten the energy transition process and address global warming and climate change.

Managing the COVID-19 pandemic in thirty-two policy measures in Saudi Arabia: A mixed-methods analysis.

BACKGROUND: The Kingdom of Saudi Arabia has developed rigorous strategies to control and prevent the spread of COVID-19. However, the effectiveness of these measures in containing and mitigating the epidemic has yet to be studied. This paper aims to assess the efficiency of preventive policy initiatives that Saudi Arabia has taken to reduce the spread of COVID-19, which was rapid and progressive in nature. Information on the effectiveness of measures applies to help the Saudi government adjust policy responses when considering which measures to relax once the epidemic is controlled. METHODS: Data for this study were retrieved via publicly available data sources such as the Saudi Arabia Ministry of Health and the government's official news agency-Saudi Press Agency (SPA) websites. Other datasets, such as prevention measures, were gathered from the Country Policy Tracker website. Our dataset's time component extends over 590 consecutive days from 20 January 2020-31 August 2021. Moreover, a mixed-method approach combining COVID-19 data and prevention measures was adopted to assess preventative measures practice. We compiled the dataset used in this study in a Microsoft Excel database. The significance of observed differences among implementing effective strategies was determined using ANOVA and Mixed methods approach. Noticeably, the statistical analysis was performed using the open-source statistical system R version 4.2 (available at http://cran.r-project.org). RESULTS: Our analysis showed that only three out of the 32 (9.4%) measures significantly reduced the spread of COVID-19. Our results also show substantial variations in the spread of COVID-19 associated with preventive measures in Saudi Arabia. There was a significant positive correlation between activating and massive testing in communities and cases of COVID-19 (measure effect = 923.086 and p < 0.05). A similar result was found for complete curfew across the Kingdom and cases of COVID-19 (measure effect = 621.389 and p < 0.10). Removing slum areas interrupted the spread of Covid-19 (measure effect = 305.689 and p < 0.01). The other preventive measures did not significantly affect the COVID-19 pandemic distribution. These findings consistently concluded that activating and massive testing in communities, complete curfew across the Kingdom, and removal of slum areas were the most effective measures for reducing the impact of the COVID-19 pandemic. CONCLUSION: Only by understanding these correlations will it be possible to control and reduce the rate of COVID-19 spread and, therefore, suggest a possible exit strategy once the epidemic is controlled.

Short-term effect of COVID-19 pandemic on cryptocurrency markets: A DCC-GARCH model analysis.

This research examines the impact of the coronavirus index on the returns and volatility of ten major cryptocurrencies during the COVID-19 pandemic. For this purpose, we applied a multivariate volatility GARCH model with an integrated dynamic conditional correlation (DCC) approach to daily cryptocurrency values observed data during the January-December, 2020 period. Moreover, we used the Granger causality test to study return-volume correlations. The findings indicate that cryptocurrency volatility declined after the World Health Organization declared on March 11, 2020, that the coronavirus was a pandemic. Unlike most of the relevant previous studies, we found that the COVID-19 crisis did not have a long-term effect on cryptocurrency returns and volatility but only presented a short-term effect. Our results have implications for investors who need to determine an optimal portfolio for a scenario other than the base.

Childhood visceral leishmaniasis in Tunisia: A cross-sectional study in local spatial analysis.

This paper describes spatial distribution of Visceral Leishmaniasis (VL) and determines its correlation with climatic factors in an endemic focus in northern and central Tunisia. Data on VL cases in children under five years of age were obtained by consulting medical reports from all Tunisian Pediatric Departments (TPD) during 2006-2016. Three key climatic factors, namely precipitation, continentality index and pluviometric coefficient of Emberger were used as predictor variables to model the VL geographical distribution. Data handling and statistical analysis were performed using R and Arcview GIS software systems. Bayesian local spatial model was employed to analyse the data. The results show a progressive increase in the VL incidence rates in regions with high levels of precipitation, but with low values of both continentality index and pluviometric coefficient of Emberger. A likely explanation of these findings arises from the opposite local effects of climatic factors which tend to cancel each other out in the calculation of the mean parameter estimate over the whole study area. We conclude that using non-local spatial analysis approach leads to misleading epidemiological interpretations, which in turn are of relevance for more efficient and cost-effective resource allocation for control and well manage the spread of VL in the study region and elsewhere in Tunisia.

Visceral leishmaniasis in Tunisia: spatial distribution and association with climatic factors.

Visceral leishmaniasis (VL) cases in children less than five years of age were recorded from 1996 through 2006 from Tunisian pediatric departments. Mean incidence rates were calculated for each of the 215 districts in the study area. Averages of annual rainfall and extreme values of low temperatures in winter and high temperatures in summer were used to characterize the climate of each district according to its continentality index and bioclimatic zone. A geographic information system and a local indicator of spatial association were used to summarize the spatial properties of VL distribution. Poisson spatial regression was performed to study the relationship between VL incidence rates and climatic parameters. We identified one hot-spot region of 35 inland districts located mostly in the semi-arid bioclimatic zone and two cold-spots located in coastal regions of the northeastern sub-humid zone and the southeastern arid zone. The incidence rate of VL was positively correlated with mean yearly rainfall and continentality index.