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This paper studies the impact of a covid-19 pandemic on the global economy. The empirical analysis of the work is based on data before and after (2019-2021) covid-19 and uses OECD data figures. The paper uses comparative analysis to find out changes during the pandemic and at the height of the pandemic, the global economy has passed the lowest point of the crisis, and it began economic growth in the leading countries of the world. This growth, like the crisis, developed in different ways in the economies of the US, EU and China. Our results show that during the pandemic, as a result of the lockdown, almost simultaneously, there was an abrupt increase in unemployment and a decline in production in all countries. In OECD countries, unemployment jumped from 5.2% in February to 8.5% in April, in the US - from 3.5% in February to 14.4% in April, in China - from 5.3% in January up to 6.2% in February. In China, both the epidemic and the economic crisis began earlier than in all other countries, respectively, the economy began to grow earlier. Our findings come to conclusion in a such way, despite the fact that, according to the forecast, the damage in the medium term will be less than after the global financial crisis, it is still significant. So, in 2024, the expected level of world production will be about 3 percent lower than before the pandemic. Countries differ in the degree of expected scarring, depending on the structure of the economy and the scale of the policy response. Emerging market and developing economies are predicted to face more palpable scars than advanced economies. [ABSTRACT FROM AUTHOR]

Today, the whole world is fighting the war against Coronavirus. The spread of the virus has been observed in almost all the parts of the world. Covid-19 also known as SARS-Cov-2 was initially observed in China which rapidly multiplied all over the world. The disease is said to spread by cough, normal cold, sneezing or when a person is in close contact with someone who is already infected. Therefore, the spread of the virus can occur when there is direct contact with an infected person or with the objects touched by the infected person. Hence, it is important to detect the contiguous spread of the virus and control it by taking appropriate measures. Several deep learning models have been used in detecting many diseases like Malaria disease, Lung infection, Parkinson's disease etc. Likewise, CNN model along with other transfer techniques is best proven to detect whether a person is infected with covid positive or not. The dataset consists of 1000 images of covid positive and normal x-rays. The proposed model has been trained and tested on the image dataset with the help of transfer learning models in order to improve the performance of the model. The models VGG-16, ResNet-50, Inception v3 and Xception have achieved an overall accuracy of 93%,82%,96% and 92% respectively. The performance of all the 4 architectures are analyzed, understood and hence presented in this paper. It is hence important to classify and detect covid positive infection and contribute towards making the world Covid-free. [ABSTRACT FROM AUTHOR]

Current situation in our country is been suffering from a dangerous disease called corona virus which is seen both creatures and human beings. SARS (Severe Acute Respiratory Syndrome) -COV is an example for corona virus.Retrosepective observation by Chinese people have seen many number of cases with start of manifestation in the month of December 2019[1].The first case was seen in china with different types of symptoms and it is known that it was a transmittable disease.Many people who are suffering with this will have an illness which is related to respiration part and it will be cured after taking the period of quarantine for 14 days, with any different kinds of treatments at home or an advice of doctors. Some people who are been affected others health issues will develop serious illness. Around 90% of the people are recovered without undergoing any special treatments because they themselves taking quarantine at home by following home remedies. But some people are being suffering with trouble breathing [5].In the month of March 11 2020 (WHO) recognized and declared it as an pandemic which has spread in many parts of the country within less span of time. This virus was first seen in a city called Wuhan, state of China. In the month of December 2019 the reports of the first COVID-19 cases have started. Yet, it is undecided exactly the spread of this virus and it is spreading very faster in humans. This paper tells about control and safety measures regarding water, sanitation, wash in covid-19 pandemic [1] and following the prevention methods critically is very essential to avoid the spread of this virus.Maintaining cleanliness, following all the rules, and advising your loved ones to do likewise for averting the transmission of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The world is going through a very hard time due to the pandemic first reported on December 2019 at Wuhan China. Vaccination process has started in different corner of world in war foot zone, still new cased are reported every day. 48 million people have already died out of this virus infection in the world. [1]. The disease caused by novel coronal virus needs to be detected at a very early stage and if proper care taken patient will survive. With Artificial Intelligence and Machine learning advancement it is possible to detect disease by analyzing symptoms. We have proposed a model to predict COVID 19 using Baggingensemble mechanism with Gaussian Naïve Bayes as base learner. [ABSTRACT FROM AUTHOR]

This study analyses return volatility for Malaysia and China's stock markets during the SARS and COVID-19 pandemics. Stock return volatility is estimated using GARCH family models (GARCH (1,1), TGARCH and EGARCH). Generally, GARCH (1,1) estimates symmetric conditions, whereas TGARCH and EGARCH estimate the asymmetric condition or leverage effect of return volatility. Stock return volatility in China and Malaysia are then compared to assess the severity of pandemic cases during the study period. Post pandemic, Malaysia is seen to experience higher decrements in leverage effect when using the TGARCH model. Conversely, the effect is higher for China when using the EGARCH model for the SARS pandemic. To aid in predicting future return volatility after the COVID-19 pandemic, return volatility after the SARS pandemic is forecast, with the forecast value serving as the basis for evaluating the error using the mean absolute error (MAE), root mean square error (RMSE) and Theil inequality coefficient (TIC) approaches. The forecast error performance is ranked to identify outperforming GARCH family models for the pandemic period: the TGARCH model for China's stock market but the GARCH model for Malaysia's stock market. [ABSTRACT FROM AUTHOR]

China is in the critical period of building a moderately prosperous society in an all-round way. It is the period of rapid economic transformation and the decisive battle for deepening the reform. China's development is facing new opportunities and challenges. The risk of foreseeable and unforeseen is increasing. Combined with the new coronavirus, (coronavirus disease 2019 is a new type of pneumonia, also named Covid-19) one of the global biological risk events, this paper analyzes the challenges faced by China in the future in response to biological risk, and discusses how to deal with microbial risk in the future, makes in-depth thinking and puts forward suggestions. [ABSTRACT FROM AUTHOR]

The very rapid development of technology today cannot be separated from the role of artificial intelligence. Artificial intelligence is a thought process or intelligence like humans' that machines can use to do specific jobs. The use of artificial intelligence is very diverse in various fields, including the health sector. Recently the world's media has been shocked by the virus spread in China, namely the coronavirus. Corona can cause severe pneumonia for sufferers. Pneumonia itself is inflammation of the lungs caused by a virus. One of pneumonia sufferers' characteristics is that there is a fluid that looks like white spots on radiology or chest X-rays. However, it will be challenging to diagnose because at a glance, the radiological test results of lungs with pneumonia and normal lungs are similar. So, misdiagnosis is possible. Therefore, this study aims to create a machine learning model to classify lungs with pneumonia and normal lung on chest radiological images using the Convolutional Neural Network (CNN) algorithm. CNN is well known as a method in which the data process both image and video. The classification obtained for validation results in an accuracy value of 94%-99% for the pneumonia category, which suggests that the model can predict the image correctly. Meanwhile, for normal lung images, the accuracy rate is 84% -98%. This shows that the model can predict almost all of the images well; there is only one image that is not predicted correctly. [ABSTRACT FROM AUTHOR]