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A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause; it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions; this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia; they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

Introduction: Coronavirus disease 2019 (COVID-19) is a novel coronavirus that was frequently isolated from Wuhan, China in patients with respiratory tract infections. The most prevalent symptoms are fever and respiratory tract involvement. In this paper, seizures, an uncommon manifestation of the condition, is described. Case Discussion: A previously healthy 34-year-old female was taken to the emergency room with a widespread tonic-clonic seizure. Five days prior to admission, the patient complained of fever, cough, headache, widespread weakness, and myalgias. She experienced two episodes of convulsions around one hour ago. The MRI of the brain was normal, whereas the CT of the chest indicated localised ground-glass opacities. Using real-time PCR, the respiratory material was positive for COVID-19. The patient's symptoms improved after using anticonvulsive and antiviral drugs. Conclusions: To our knowledge, this is one of the rare case studies reporting a link between frequent seizures and COVID - 19. [ABSTRACT FROM AUTHOR]

COVID-19 virus is transmitted from human to human through splashes of saliva when the sufferer coughs, sneezes, or talks and is inhaled by the people around him. The spread of this virus started from the Chinese city of Wuhan in December 2019 which quickly spread to cities outside Wuhan including Jakarta, Indonesia. The same is in other countries, the local government in Jakarta taking some emergency decisions to minimize the virus spreading including the movement control order (MCO). This paper discusses the simulation of COVID-19 spreading in the early stage in Jakarta using a different model of disease spreading available on The Spatiotemporal Epidemiologic Modeler (STEM). The focus of this simulation is to see the effect of MCO on the COVID-19 spreading in the local area in Indonesia. The result shows that the MCO contribute to the decreasing of case number with some certain period of lag time. [ABSTRACT FROM AUTHOR]

This paper manages the Origin of Covid (SARS-CoV-2-novel Covid) and the Vaccination view. Within excess of 10 million tainted cases and more than 3 million setbacks at the hour of composing this piece, the original Covid actuated wellbeing disease has arisen as the most serious among every one of the worldwide pandemics. The novel (COVID-19 or 2019-nCoV) was found in Wuhan, Hubei Province, China. Bats were liable for the underlying spread, which was subsequently spread to people by the raccoon canine and palm civet. The normal COVID-19 manifestations are fever, dry hack, and sleepiness. The significant manifestations are trouble breathing, chest torment, and loss of speech. The indications and indications of SARS-CoV-2 prompting COVID-19 completely match those of the occasional or, dust sensitivities and flu as migraine, touchiness of the throat, dry hack, weariness, fever, and at times loss of sensation (Tu et al., year2020). An individual tainted with the diseases of sensitivity to pollen or, flu sensitivity is additionally liable to show temperature that can be recorded with thermo-scanners prompting the individual to turn into a suspect of the sickness. Along these lines, before immunizing, a fast and exact analytic meter or, the pack is the need of great importance to identify the SARS-CoV-2 inferable from the way that the testing in view of PCR is tedious and exorbitant (Sirkeci and Yucesahin, year2020). [ABSTRACT FROM AUTHOR]