Empirical Analysis Of Covid-19 On Brics Nations And Their Impact Positive/Negative On Stock Market Indices

The coronavirus is a novel disease affected across the world. The symptoms are cold and fever. It was invented in December 2019 by China and is denoted as Covid-19 by World Health Organization (WHO). This disease affected not only humans but also influenced the stock market and an economic crisis across the world. About 3% effect on the global economy, it was comparatively higher than 2008-09. 1.2% and 2.8% were affected by middle Asia and the middle east during 2019-20. The oil export and import countries were affected by about 4.2% and 0.7% (IMF, 2020). The stock market is one of the barometers for Post Covid-19 status measurement. The study aims to analyze the infected cases, investigate the stock market indices in BRICS Nations, and find the relations between infected situations and the stock market of BRICS Nations. This study uses secondary data from the official websites of the World Health Organization, Dashboard from January 2021-June 2022. Using Multi Regression analysis in the five nations were infected and death cases and stock market indices. This study will be helpful in the present situation, and investors will decide to diversify funds in the profitable sector to accelerate their wealth. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Covid-19 Prediction Using FbProphet

Currently, the world is facing a pandemic situation due to the outbreak of the most infectious newly discovered disease called Covid-19. The outbreak of this disease happened at the least expected time and is holding a large impression on our lives. This disease-causing virus Corona has the property of doubling each day. Being a contagious disease this virus spreads through the droplets of the infected person. As a consequence, the infected person acts as a carrier and contaminates the other. Due to the doubling property, theinfected people's growth rises exponentially. Henceforth we need a strong model that could predict the growth of this virus to keep it at the handle. Our project aims at building a machine learning model that could predict the spread of this virus in the next seven days. For forecasting mechanism, a module called FbProphet is used which is exclusive for time series prediction. © 2022 IEEE.

Overview of dreadful consequences of SARS-CoV-2 invasion in Italy from March 2020 to March 2022.

Background: The unpredicted pandemic disease COVID-19 first flared up adversely in Europe by imparting interminable force of infected and fatality cases to Italy. In late February 2020, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in northern Italy and swiftly proliferated to the entire country, albeit continuous to date (23 March 2022) with a lesser extent of deadliness. Current review focused on the invasions and the associated consequences by SARS-CoV-2 during the period of March 2020-March 2022. Main body of the abstract: Initially, the lethality and transmissibility of the novel virus made Italy stunned within 1 month, the number of death cases reached 12,428 at the end of March 2020. The Italian Government announced an immediate emergency phase in entire country, educational institutions to local businesses, manufacturing works, cultural activities to elective activities were rescinded and all the hospitals to morgues were swamped, ensuing that fear of epidemic was impended. Besides, the Italian National Health System and Service coordinated massive public health interventions and conferred unprecedented efforts to limit the high mortality rate of the first wave of infection. Amidst 2 years of epidemic (as of 23 March 2022), Italy has documented 14,070,450 (23.74% of the population) confirmed infected cases, 12,685,306 (21.41% of the population) healed cases, 158,254 death cases (0.27% of the population) and ranking 9th worldwide in the number of deaths. Short conclusion: Based on publicly available Italian Ministry of Health COVID-19 data, current review has comprehended region-wise total infected cases, death cases and healed cases for three consecutive years 2020-2022 to foresee different patterns of the regional outbreak and gradual subservience. At a glance, we highlighted the overview of the exhaustion and exertion of COVID-19 crisis throughout the periods in Italy.

Do macroeconomic shocks in the local labor market lead to child maltreatment and death?: Empirical evidence from Japan.

BACKGROUND: Japan is facing a rapid increase in the number of reported child maltreatment cases. Child maltreatment has long-term consequences for the victims, and unemployment rate is considered a strong predictor of it. However, only few studies have analyzed the causal relation between child maltreatment and the unemployment rate-particularly the effects of the latter on the former-in Japan. METHODS: Using prefecture-level longitudinal data from 2005 to 2016, we employed a fixed effects instrumental variable estimation. The estimation included a weighted average of the national unemployment rate across industries by industrial structures in 2005 as an instrument to identify the causal effects. RESULTS: The average local unemployment rate changed by approximately 50% from the peak to the bottom in the sample period. A 50% increase in local unemployment rates increased the number of reported child neglect cases and child deaths by 80% and 70% (statistically significant at the 5% level), respectively. Further, it increased cases of death due to external causes, unintentional injuries, and unintentional drowning by 146%, 217%, and 315% (statistically significant at the 5% level), respectively. CONCLUSION: The local unemployment rate is a risk factor for child maltreatment, resulting in children's death, especially as a result of unintentional drowning-the common cause of death due to child neglect. When the local unemployment rates rise, governments should allocate more financial and human resources for preventive measures to combat child deaths caused by neglect.

Nigerian COVID-19 Incidence Modeling and Forecasting with Univariate Time Series Model

The occurrence of COVID-19 has given rise to dreadful medical difficulties due to its hyper-endemic effects on the human population. This made it fundamental to model and forecast COVID-19 pervasiveness and mortality to control the spread viably. The COVID-19 data used was from February, 28, 2020 to March 1, 2021. ARIMA(1,2,0) was selected for modeling COVID-19 confirmed and ARIMA(1,1,0) for death cases. The model was shown to be adequate for modeling and forecasting Nigerian COVID-19 data based on the ARIMA model building results. The forecasted values from the two models indicated Nigerian COVID-19 cumulative confirmed and death case continues to rise and maybe in-between 189,019–327,426 and interval 406–3043, respectively in the next 3 months (May 30, 2021). The ARIMA models forecast indicated an alarming rise in Nigerian COVID-19 confirmed and death cases on a daily basis. The findings indicated that effective treatment strategies must be put in place, the health sector should be monitored and properly funded. All the protocols and restrictions put in place by the NCDC, Nigeria should be clung to diminish the spread of the pandemic and possible mortality before immunizations that can forestall the infection is developed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Day-Level Forecasting for Coronavirus Disease (COVID-19)

Coronavirus (COVID-19) recently spread quickly all over the world. Most infected people with the coronavirus will experience mild to moderate respiratory illness, but elderly people and those with chronic diseases are more likely to suffer from serious disease, often leading to death. According to the Egyptian Ministry of Health, there are 96,336 confirmed infected cases with coronavirus and 5,141 confirmed deaths from the current outbreak. Accurate forecasting of the spread of confirmed and death cases as well as analysis of the number of infected and deaths are crucially required. The present study aims to explore the usage of support vector machine (SVM) in the prediction of coronavirus infected and death cases in Egypt, which helps in the decision-making process. The forecasting model suggest that the number of coronavirus cases grows exponentially in Egypt and more efforts shall be directed to increase the public awareness with this disease. The proposed method is shown to achieve good accuracy and precision results.

Regression modeling and correlation analysis spread of COVID-19 data for Pakistan

This study presents a mathematical analysis of the coronavirus spread in Pakistan by analyzing the (COVID-19) situation in six provinces, including Gilgit Baltistan, Azad Jammu Kashmir and federal capital (seven zones) individually. The influence of each province and the Federal Capital territory is then observed over the other territories. By subdividing the associated data into confirmed cases, death cases, and recovery cases, the dependence of the (COVID-19) situation from one province to the other provinces is investigated. Since the worsening circumstance in the neighboring countries were considered as a catalyst to initiate the outburst in Pakistan, it seemed necessary to have an understanding of the situation in neighboring countries, particularly, Iran, India, and Bangladesh. Exploratory data analysis is utilized to understand the behavior of confirmed cases, death cases, and recovery cases data of (COVID-19) in Pakistan. Also, an understanding of the pandemic spread during different waves of (COVID-19) is obtained. Depending on the individual situation in each of the provinces, it is expected to have a different ARIMA model in each case. Hunt for the most suitable ARIMA models is an essential part of this study. The time-series data forecasts by processing the most suitable ARIMA models to observe the influence of one territory over the other. Moreover, forecasting for the month of August 2021 is performed and a possible correlation with actual data is determined. Linear, multiple regression, and exponential models have been applied and the best-fitted model is acquired. The information obtained from such analysis can be employed to vary possible parameters and variables in the system to achieve optimal performance. © 2022 The Authors.

Day-Level Forecasting for Coronavirus Disease (COVID-19)

Corona virus (COVID-19) was recently spread quickly all over the world. Most infected people with the Corona virus may experience mild to moderate respiratory illness, but elderly people, and those with chronic diseases are more likely to suffer from serious disease, often leading to death. According to the Egyptian Ministry of Health, there are 96336 confirmed infected cases with Corona virus and 5141 confirmed deaths from the current outbreak. Accurate forecasting of the spread of confirmed and death cases as well as analysis of the number of infected and deaths are crucially required. The present study aims to explore the usage of support vector machine (SVM) in the prediction of coronavirus infected and death cases in Egypt which help in decision-making process. The forecasting model suggest that the number of coronavirus cases grows exponentially in Egypt and more efforts shall be directed to increase the public awareness with this disease. The proposed method is shown to achieve good accuracy and precision results.

Place-Specific Corona Dashboards for Health Policy: Design and Application of a ‘Dutchboard’

Since the outbreak of the corona virus in the end of 2019, many worldwide attempts have been made to monitor and control the COVID-19 pandemic. A wealth of empirical data has been collected and used by national health authorities to understand and mitigate the spread and impacts of the corona virus. In various countries this serious health concern has led to the development of corona dashboards monitoring the COVID-19 evolution. The present study aims to design and test an extended corona dashboard, in which—beside up-to-date daily core data on infections, hospital and intensive care admissions, and numbers of deceased people—also the evolution of vaccinations in a country is mapped out. This dashboard system is next extended with time-dependent contextual information on lockdown and policy stringency measures, while disaggregate information on the geographic spread of the COVID-19 disease is provided by means of big data on contact intensity and mobility motives based on detailed Google Mobility data. Finally, this context-specific corona dashboard, named ‘Dutchboard’, is further extended towards the regional and local level so as to allow also for space-specific ‘health checks’ and assessments.

Effect of mutation and vaccination on spread, severity, and mortality of COVID-19 disease.

Coronavirus disease 2019 (COVID-19) has had different waves within the same country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-data sets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for severe acute respiratory syndrome coronavirus 2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, USA, UAE, Poland, Colombia, Turkey, and Switzerland). The total number of confirmed cases and death cases in different waves of COVID-19 were compared to that of the previous one for equivalent periods. The total number of death cases in each wave was presented as a percentage of the total number of confirmed cases for the same periods. In all the selected 12 countries, Wave 2 had a much higher number of confirmed cases than that in Wave 1. However, the death cases increase was not comparable with that of the confirmed cases to the extent that some countries had lower death cases than in Wave 1, UAE, and Spain. The death cases as a percentage of the total number of confirmed cases in Wave 1 were much higher than that in Wave 2. Some countries have had Waves 3 and 4. Waves 3 and 4 have had lower confirmed cases than Wave 2, however, the death cases were variable in different countries. The death cases in Waves 3 and 4 were similar to or higher than Wave 2 in most countries. Wave 2 of COVID-19 had a much higher spread rate but much lower severity resulting in a lower death rate in Wave 2 compared with that of the first wave. Waves 3 and 4 have had lower confirmed cases than Wave 2; that could be due to the presence of appropriate treatment and vaccination. However, that was not reflected in the death cases, which were similar to or higher than Wave 2 in most countries. Further studies are needed to explain these findings.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators.

The coronavirus has a high basic reproduction number ( R 0 ) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

COVID-19 Death and BCG Vaccination Programs Worldwide.

Several clinical trials are being conducted worldwide to investigate the protective effect of the bacillus Calmette-Guérin (BCG) vaccine against death in healthcare providers who are working directly with coronavirus disease 2019 (COVID-19) patients. Clinical studies suggested that certain live vaccines, particularly the BCG vaccine, could reduce the mortality due to other diseases caused by non-targeted pathogens, most probably through the nonspecific effects (heterologous effects). By the end of May 2020, the available information on the COVID-19 pandemic indicated the great effect of the BCG vaccine in reducing the number of COVID-19 death cases. The occurrence of death due to COVID-19 was found to be 21-fold lower in countries with a national BCG vaccination policy than in countries without such a policy, based on the medians of COVID-19 death case per 1 million of the population in these two groups of countries (p<0.001, MannWhitney test). Therefore, it can be concluded that the early establishment of a BCG vaccination policy in any country is a key element in reducing the number of COVID-19 and tuberculosis death cases.

Characteristics of mortal COVID-19 cases compared to the survivors.

The outbreak of coronavirus disease 2019 (COVID-19) initially occurred in December 2019 and triggered a public health emergency. The increasing number of deaths due to this disease was of great concern. Therefore, our study aimed to explore risk factors associated with COVID-19 deaths. After having searched the PubMed, EMBASE, and CNKI for studies published as of August 10, 2020, we selected articles and extracted data. The meta-analysis was performed using Stata 16.0 software. Nineteen studies were used in our meta-analysis. The proportions of comorbidities such as diabetes, hypertension, malignancies, chronic obstructive pulmonary disease, cardio-cerebrovascular disease, and chronic liver disease were statistically significantly higher in mortal COVID-19 cases. Coagulation and inflammatory markers, such as platelet count, D-dimer, prothrombin time, C-reactive protein, procalcitonin, and interleukin 6, predicted the deterioration of the disease. In addition, extracorporeal membrane oxygenation and mechanical ventilation predicted the poor prognosis during its progression. The COVID-19 pandemic is still evolving, placing a huge burden on healthcare facilities. Certain coagulation indicators, inflammatory indicators, and comorbidities contribute to the prognosis of patients. Our study results may help clinicians optimize the treatment and ultimately reduce the mortality rate.

Clinical characteristics of the first known cases of death caused by COVID-19 pneumonia.

Severe pneumonia caused by COVID-19 has resulted in many deaths worldwide. Here, we analyzed the clinical characteristics of the first 17 reported cases of death due to COVID-19 pneumonia in Wuhan, China. Demographics, initial symptoms, complications, chest computerized tomography (CT) images, treatments, and prognoses were collected and analyzed from the National Health Committee of China data. The first 17 reported deaths from COVID-19 were predominately in older men; 82.35% of patients were older than 65 years, and 76.47% were males. The most common initial symptoms were fever or fatigue (14 cases, 82.35%), respiratory symptoms, such as cough (12 cases, 70.59%), and neurological symptoms, such as headache (3 cases, 17.65%). The most common finding of chest CT was viral pneumonia (5 cases, 29.41%). Anti-infectives (11 cases, 64.71%) and mechanical ventilation (9 cases, 52.94%) were commonly used for treatment. Most of the patients (16 cases, 94.12%) died of acute respiratory distress syndrome (ARDS). Our findings show that advanced age and male gender are effective predictors of COVID-19 mortality, and suggest that early interventions to reduce the incidence of ARDS may improve prognosis of COVID-19 pneumonia patients.

Epidemiologic and clinical characteristics of 42 deaths caused by SARS-CoV-2 infection in Wuhan, China: A retrospective study.

This study described the epidemiologic and clinical characteristics of patients who died from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and pointed out the potential risk factors associated with fatal outcomes. Retrospective data from 42 death cases due to SARS-CoV-2 infection at Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, China was analyzed. Demographics, clinical detection, laboratory findings, and treatments of the deceased were collected and analyzed. The average time between onset of symptoms and admission to the hospitals was 11 ± 5 days of hospitalization. Among the deceased, 60% were with co-morbidities. All of them were having fever and bilateral pneumonia on computed tomography, abnormal infection-related biomarkers, and renal impairment. Abnormal blood coagulation parameters that appeared in more than half of them, were consistent with disseminated intravascular coagulation. All of the patients were treated in the ICU. Based on the fact that SARS-CoV-2 infection carries a risk of mortality, we may infer a few older male patients with underlying comorbidities are likely to have the increased risk. Impaired consciousness level, markers of renal impairment and coagulation abnormalities may be poor prognostic factors.

Invasion of novel corona virus (COVID-19) in Indian territory.

Coronavirus (COVID-19) started its invasion as an epidemic from Wuhan, China and propagated to become the scary pandemic that reached more than 200 countries all over the world. High number of infected people and unfortunately high mortality are the result of this invasion. The Indian scenario is no exception to this deadly infection attack, though it started a bit late. The first case in India came into notice in January and the number of cases showed an enormous growth in mid March and still continue to grow. This timely report focuses on the current invasion scenario in India as of 11th May 2020; with total cases of 67,152, active cases of 44, 029, deaths totaling to 2206 and over-all recoveries of ∼20,917 patients.