Murdockmyrick5107

Mobility, group awareness, and temperature are considered as the important factors that may impact the increase in confirmed cases of the COVID-19[1]. This paper aims to verify the above factors on the COVID-19 and show the possible confounding factors of each research variable in reality. Based on this, we collected data about the epidemic from January 20, 2020 to February 24, 2021, including the relevant data of 31 provinces and regions in China. Plus, we use the directed acyclic graph (DAG)[2] to show the causal relationship between the above influencing factors and the confirmed daily epidemic cases, and the confounding is estimated based on DAG. The effective adjustment set of factors are used to perform the regression of the total causal effect among the explanatory variables and the confirmed cases of the epidemic using negative binomial regression. Through the comprehensive causal analysis of the decisive factors for the COVID-19, we provide strong evidence for population mobility, group awareness and the impact of weather on the epidemic, and estimates the possible confounding factors in all aspects of society. Incorporating the above factors, we provide suggestions for future decisions on the prevention of large-scale epidemics.With the expansion of coronavirus in the World, the search for technology solutions based on the analysis and prospecting of diseases has become constant. The paper addresses a machine learning algorithms analysis used to predict and identify infected patients. For analysis, we use a multicriteria approach using the PROMETHEE-GAIA method, providing the structuring of alternatives respective to a set of criteria, thus enabling the obtaining of their importance degree under the perspective of multiple criteria. The study approaches a sensitivity analysis, evaluating the alternatives using the PROMETHEE I and II methods, along with the GAIA plan, both implemented by the Visual PROMETHEE computational tool, exploring numerical and graphical resources. The analysis model proves to be effective, guaranteeing the ranking of alternatives by inter criterion evaluation and local results with intra criterion evaluation, providing a transparent analysis concerning the selection of prediction algorithms to combat the COVID-19 pandemic.Under the influence of COVID-19, the global economic and social development is facing great challenges. With the increase of government financial pressure and the decrease of debt paying ability, the problem of debt risk of local governments in China is attracting wide attention. In order to measure the level of China's local government debt risk under the influence of COVID-19, this paper takes China's Sichuan Province as an example, collects the core indicators data of measuring local government debt risk in 2017-2020 years, and uses AHP-TOPSIS method to make a comprehensive analysis of the local government debt risk situation in different periods before and after COVID-19. It is found that the local government debt risk in Sichuan Province is generally controllable. However, influenced by COVID-19, in 2020, the overall level of local government debt risk in Sichuan province expanded by 22.1% compared with 2019, this is mainly due to the further expansion of debt scale and slower economic growth. This paper suggests that the Chinese government should speed up the construction of comprehensive early warning and supervision system of local government debt risk, and prevent and resolve the debt risk of local government in advance.The pandemic generated by the Sars Cov 2 corona virus is monitored, at the level of each country, every day, by several COVID-19 indicators. The present paper proposes a Group Multi-Criteria (GMC) approach for the development of a country COVID-19 indicator called COPACOV (COuntry Performance Against COVID-19) indicator. It is calculated starting from several country COVID-19 indicators measured separately, in a set a countries. COPACOV can identify which countries are more vulnerable to COVID-19 illnesses from several points of view taken together. The GMC approach is based on a hybrid method composed from the Group Analytic Hierarchy Process (GAHP) weighting method and from the Multicriteria Optimization and Compromise Solution (VIKOR) multi-criteria method. The aggregation (consensus) of the experts' opinions, in the GAHP method is calculated with the geometric mean method. GW5074 mouse The VIKOR method uses the weights calculated by the GAHP method and calculates the COPACOV indicators. The proposed GMC approach is applied in a case study for a set of COVID-19 indicators and a set of Eastern European countries.This paper studied the impact of COVID-19 on China's capital market and major industry sectors via an improved ICSS algorithm, a time series model with the exogenous variable and a non-parametric conditional probability estimation. Through the empirical analysis, it is found that the epidemic has no significant impact on the return of the stock and bond markets, but it has increased the market volatility and the impact on the stock market volatility is gradual and more obvious. There are significant differences in the significance, direction and duration of the epidemic on different sectors. In addition, the impact of COVID-19 has been gradual in some industries and rapid in others. Different industries show different sensitivities in their response to COVID-19. Based on the analysis of the impact, this paper put forward the corresponding suggestions for investment strategies and macro-control decisions.The pandemic caused by the new coronavirus has brought to light a series of concerns for the Brazilian population and government departments due to the different costly consequences that it has generated. It has also mobilized different strategic fronts that plan and implement several mitigating measures against the virus. Besides, the search for solutions for adequate care for individuals in need of support has been constant. This work uses ELECTRE-MOr, a Multi-Criteria Decision Aid (MCDA) method, to support the logistic plan for the vaccine distribution throughout Brazil, essentially to remote areas. The method allows an objective and structured classification of ideal types of thermal boxes for the storage of immunobiological inside the Cold Chain, presenting the best alternative that maintains the quality of materials until the final destination and has the best cost-benefit. Currently, the ELECTRE-MOr model is under development in a computational tool in Python, allowing the use of the method intuitively and clearly, enabling professionals of any area of expertise to apply it.This article is the third in a series of historical reviews on sub-Saharan Africa (SSA), exploring why agricultural production and irrigation schemes are underperforming, and how this contributes to high levels of food insecurity. The expression 'food security' emerged in 1974 following the Sahel and Darfur famines. Despite SSA being a net agricultural exporter, food insecurity has persisted and is increasing. This is largely a legacy of the export-oriented colonial agricultural production systems, which procured scarce fertile land, water and labour to meet the needs of industries and consumers in the Global North. Colonialism also undermined the social contract between traditional leaders and communities, which had been instrumental in managing food scarcity in earlier times. Post-independence, agricultural policies remained focused on exports and neglected critical research and investment integrating food productions systems into the domestic economy; developing supply chains and associated market, storage and value-adding infrastructure; and introducing appropriate technologies. As a result, Africa is the only region in the world where increased export production caused a decline in per capita food production. African nations should be extracted from the debt accrued due to poor colonial investments, World Bank lending practices, and global currency and interest fluctuations, which have crippled their capacity to support agriculture and improve livelihoods and food security. Farming needs to be profitable, which includes farmers being connected to domestic supply chains and market signals, local value-adding, and post-harvest storage. This will create jobs and increase income earning capacity, which is the key to households' food security.

Fever, cough, fatigue, and myalgia are usually the original clinical picture of the COVID-19 pandemic, which appears non-specific and not exclusive.

To illustrate the clinical picture pattern and assess the prevalence of underlying co-morbidities and their correlation with the severity of COVID-19 infected patients.

A cross-sectional online survey included 580 participants who were either suspected or confirmed with COVID-19 infection.

The severity of the disease significantly correlates with both age (p=.01) and the time lag of the diagnosis of COVID-19 (p=.03). Hypertension (p=.015) and diabetes mellitus (p<.01) were significantly associated with the duration of symptoms. A wide range of ages (21-60 years) seemed to be the only risk factor for the severity. When symptoms were tested, dyspnea appeared to be the most prevalent symptom, predicting a more severe disease (OR= .066, 95% CI .022- .200), followed by diarrhea (OR= .285, 95% CI .122-.663), then fever (OR= .339, 95% CI .139-.824). During the examination of co-morbidities influences on the severity, the only major co-morbidity that predicted a more severe disease was IHD (OR= .218, 95% CI .073- .648), p= .006.

Special consideration is required for patients with COVID-19 with an associated longer gap between symptoms and diagnosis and associated co-morbidities including hypertension, diabetes, and established chronic kidney disease (CKD), for which this study proved its profound influence on the severity of the illness and duration of symptoms.

Special consideration is required for patients with COVID-19 with an associated longer gap between symptoms and diagnosis and associated co-morbidities including hypertension, diabetes, and established chronic kidney disease (CKD), for which this study proved its profound influence on the severity of the illness and duration of symptoms.There is a rising concern with social bots that imitate humans and manipulate opinions on social media. Current studies on assessing the overall effect of bots on social media users mainly focus on evaluating the diffusion of discussions on social networks by bots. Yet, these studies do not confirm the relationship between bots and users' stances. This study fills in the gap by analyzing if these bots are part of the signals that formulated social media users' stances towards controversial topics. We analyze users' online interactions that are predictive to their stances and identify the bots within these interactions. We applied our analysis on a dataset of more than 4000 Twitter users who expressed a stance on seven different topics. We analyzed those users' direct interactions and indirect exposures with more than 19 million accounts. We identify the bot accounts for supporting/against stances, and compare them to other types of accounts, such as the accounts of influential and famous users. Our analysis showed that bot interactions with users who had specific stances were minimal when compared to the influential accounts.