Coronaviruses transmit COVID-19, a rapidly spreading disease. A Coronavirus infection (COVID-19) was first discovered in December 2019 in Wuhan, China, and spread rapidly throughout the planet in exactly some months. because of this, the virus can cause severe symptoms and even death, especially within the elderly and in people with medical conditions. The virus causes acute respiratory infections in humans. the primary case was diagnosed in China in 2019 and the pandemic started in 2020. Since the quantity of cases of COVID-19 is increasing daily, there are only a limited number of test kits available in hospitals. So, to stop COVID-19 from spreading among people, an automatic diagnosis system must be implemented. during this study, three pre-trained neural networks supported convolutional neural networks (VGG16, VGG19, ResNet50) are proposed for detecting Coronavirus pneumonia infected patients through X-rays and computerized tomography (CT). By using cross-validation, we've got implemented binary classifications with two classes (COVID-19, Normal (healthy)). Taking into consideration the results obtained, the pre-trained ResNet50 model provides the simplest classification performance (97.77% accuracy, 100% sensitivity, 93.33% specificity, 98.00% F1-score) among the opposite three used models over 6259 images.

The present world is badly affected by novel coronavirus (COVID-19). Using medical kits to identify the coronavirus affected persons are very slow. What happens in the next, nobody knows. The world is facing erratic problem and do not know what will happen in near future. This paper is trying to make prognosis of the coronavirus recovery cases using LSTM (Long Short Term Memory). This work exploited data of 258 regions, their latitude and longitude and the number of death of 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect on extracting highly essential features for time series data (TSD) analysis.There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start [2]. Shashwat Sanket and colleagues set out to find an easy, fast, and accurate alternative using simple chest X-ray images.

A hallmark of artificial intelligence is its ability to learn from the past. As researchers advance and refine AI applications, it could increasingly become part of routine research, too--the type of work that supported the advances toward tackling this pandemic and can support the response to a future one, too. Finding meaning in a sea of messy or incomplete data is precisely what data scientists at Pacific Northwest National Laboratory (PNNL) do. With expertise in applying graph-based machine learning, detailed molecular modeling, and explainable AI to questions of national security and basic science, PNNL researchers are now turning their artificial intelligence tools to the study of fundamental questions about treatments for COVID. What they are learning sharpens the tools available in the computational toolbox for responding quickly to a future pandemic.

When the novel coronavirus led to a global pandemic last year, doctors and researchers rushed to learn as much as possible about the virus and how our bodies respond to it. They needed a lot of information, and they needed it fast. Doctors studied whether available medicines could effectively treat the symptoms of COVID-19. Virologists, biologists, and chemists scrambled to understand how the virus affects the molecular workings of cells, information key to designing medicine to treat infection and resulting disease. Medical and biological data flowed fast and furiously.

Cognitive disorders, including dementia, are increasingly being reported as a complication of the highly contagious SARS-CoV-2 virus that causes Covid-19, researchers behind the recent study at the Cleveland Clinic in Ohio have revealed. "Reports of neurological complications in Covid-19 patients and'long-hauler' patients whose symptoms persist after the infection clears are becoming more common, suggesting that [the virus] may have lasting effects on brain function," said the authors of the study, which was published this week in the journal Alzheimer's Research & Therapy. The researchers' aim was to uncover the mechanisms responsible for brain-associated complications such as delirium and the loss of taste or smell that are often found in novel coronavirus patients. In order to do so, they compared on a molecular level the host genes of Covid-19 and those responsible for some neurological disorders. Having collected the data of both Covid-19 patients and people suffering from Alzheimer's disease, they used artificial intelligence to measure the proximity between them.

Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.

Japan will commence random mass PCR testing as early as March as part of efforts to ascertain the extent of the novel coronavirus' spread in city areas, according to government sources. The central government is aiming to conduct up to several thousand polymerase chain reaction tests per day in Tokyo, Osaka and other metropolitan areas seeing a high number of cases, with the goal of using the information to develop effective virus prevention measures, the sources said Monday. In contrast with local government testing that only targets people who show symptoms or have had close contact with infected individuals, random people will be tested to determine how much the virus has spread in a particular city. The costs of the tests, which will be carried out by contracted private companies, will be fully covered by the central government. The tests are expected to be conducted at airports, as well as places where crowds tend to gather such as city centers, companies and universities.

Japan will commence random mass PCR testing as early as March as part of efforts to ascertain the extent of the novel coronavirus' spread in city areas, government sources said Monday. The central government is aiming to conduct up to several thousand polymerase chain reaction tests per day in Tokyo, Osaka and other metropolitan areas seeing a high number of cases, with the goal of using the information to develop effective virus prevention measures, the sources said. In contrast with local government testing that only targets people who show symptoms or have had close contact with infected individuals, random people will be tested to determine how much the virus has spread in a particular city. The costs of the tests, which will be carried out by contracted private companies, will be fully covered by the central government. The tests are expected to be conducted at airports, as well as places where crowds tend to gather such as city centers, companies and universities.

For Dr Mary-Anne Hartley, a medical doctor and researcher in EPFL's intelligent Global Health group (iGH), 2020 has been relentless. "It's not a relaxing time to study infectious diseases," she explained. Since the beginning of the COVID-19 pandemic, Dr Hartley's research team has been working non-stop with nearby Swiss university hospitals on two major projects. Using artificial intelligence (AI), they have developed new algorithms that, with data from ultrasound images and auscultation (chest/lung) sounds, can accurately diagnose the novel coronavirus in patients and predict how ill they are likely to become. "We've named the new deep learning algorithms DeepChest – using lung ultrasound images – and DeepBreath – using breath sounds from a digital stethoscope. This AI is helping us to better understand complex patterns in these fundamental clinical exams. So far, results are highly promising," said Professor Jaggi.