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      A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

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          Abstract

          The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.

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          A new coronavirus associated with human respiratory disease in China

          Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.
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            Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

            Background Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Methods From January 6 to February 6, 2020, 1014 patients in Wuhan, China who underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCR results. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas. A translation of this abstract in Farsi is available in the supplement. - ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.
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              Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle

              Since December 2019, a total of 41 cases of pneumonia of unknown etiology have been confirmed in Wuhan city, Hubei Province, China. Wuhan city is a major transportation hub with a population of more than 11 million people. Most of the patients visited a local fish and wild animal market last month. At a national press conference held today, Dr Jianguo Xu, an academician of the Chinese Academy of Engineering, who led a scientific team announced that a new‐type coronavirus, tentatively named by World Health Organization as the 2019‐new coronavirus (2019‐nCoV), had caused this outbreak. 1 The 2019‐nCoV has a different coronavirus‐specific nucleic acid sequence from known human coronavirus species, which are similar to some of the beta coronaviruses identified in bats. 2 , 3 The virus‐specific nucleic acid sequences were detected in lung fluid, blood and throat swab samples in 15 patients and the virus that was isolated showed a typical coronavirus appearance under electron microscopy. Further research will be conducted to better understand the new coronavirus to develop antiviral agents and vaccines. 4 We applauded the excellent job that has been done so far. The infection was first described in December. Within 9 days, a special team consisted of physicians, scientists and epidemiologists who ruled out several extremely contagious pathogens including SARS, which killed hundreds of people more than a decade ago, and MERS. This has surely alleviated environmental concerns as Hong Kong authorities had quickly stepped up the disinfection of trains and airplanes and checks of passengers due to this outbreak. Most of the patients visited the fish and wild animal market last month in Wuhan. This fish and wild animal market also sold live animals such as poultry, bats, marmots, and snakes. All patients received prompt supportive treatment in quarantine. Among them, seven patients were in serious condition and one patient died. All of the 42 patients so far confirmed were from China except one Thailand patient who was a traveler from Wuhan. Eight patients have been cured of the disease and were discharged from the hospital last week. The 2019‐nCoV now have been isolated from multiple patients and appears to be the culprit. But the mystery has not been completely solved yet. Until there is a formal published scientific manuscript, the facts can be argued, particularly regarding causality despite these facts having been officially announced. The data collected so far is not enough to confirm the causal relationship between the new‐type coronavirus and the respiratory disease based on classical Koch's postulates or modified ones as suggested by Fredricks and Relman. 5 The viral‐specific nucleic acids were only discovered in 15 patients, and successful virus culture was extremely limited to only a few patients. There remains considerable work to be done to differentiate between colonization, shedding, and infection. Additional strains of the 2019‐nCoV need to be isolated to study their homologies. It is expected that antigens and monoclonal antibodies will be developed so serology can be used to confirm previous and acute infection status. The episode demonstrates further the need for rapid and accurate detection and identification methods that can be used in the local hospitals and clinics bearing the burden of identifying and treating patients. Recently, the Clinical Laboratory Improvement Amendments (CLIA) of 1988 has waived highly sensitive and specific molecular devices known as CLIA‐waived devices so that these devices are gradually becoming available for point of care testing. Finally, the epidemiological similarity between this outbreak and that of SARS in 2002‐2003 6 is striking. SARS was then traced to animal markets 7 and eventually to palm civets. 8 Later bats were identified as animal reservoirs. 9 Could this novel coronavirus be originated from wild animals? The family Coronaviridae includes two subfamilies. 10 One, the subfamily Coronavirinae, contains a substantial number of pathogens of mammals that individually cause a remarkable variety of diseases, including pneumonia. In humans, coronaviruses are among the spectrum of viruses that cause the common cold as well as more severe respiratory disease—specifically SARS and MERS, which are both zoonoses. The second subfamily, Torovirinae, contains pathogens of both terrestrial and aquatic animals. The genus Torovirus includes the type species, equine torovirus (Berne virus), which was first isolated from a horse with diarrhea, and the Breda virus, which was first isolated from neonatal calves with diarrhea. White bream virus from fish is the type species of the genus Bafinivirus. However, there is no evidence so far that the seafood from the fish and animal market caused 2019‐nCoV‐associated pneumonia. This epidemiologic similarity clearly provides a starting point for the further investigation of this outbreak. In the meantime, this fish and animal market has been closed until the epidemiological work determines the animal host of this novel coronavirus. Only then will the miracle be complete.
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                Author and article information

                Contributors
                Journal
                IEEE Trans Artif Intell
                IEEE Trans Artif Intell
                0078300
                TAI
                ITAICB
                Ieee Transactions on Artificial Intelligence
                IEEE
                2691-4581
                February 2023
                11 January 2022
                : 4
                : 1
                : 44-59
                Affiliations
                [1] departmentDepartment of Computer Science and Engineering, institutionIndian Institute of Technology (BHU), institutionringgold 79203; Varanasi 221005 India
                Article
                TAI-2021-Nov-R-00567
                10.1109/TAI.2022.3142241
                9983698
                36908643
                4dedabe1-acfa-4d4e-b9c9-f89f0ec89048
                Copyright @ 2022

                This article is free to access and download, along with rights for full text and data mining, re-use and analysis.

                History
                : 08 November 2021
                : 25 December 2021
                : 20 January 2023
                Page count
                Figures: 9, Tables: 4, References: 104, Pages: 16
                Funding
                Funded by: institutionICSSR Project under the Ministry of Education, Government of India;
                Award ID: ICSSR/811/14/2021-22
                This work was supported by the ICSSR Project under the Ministry of Education, Government of India under Grant ICSSR/811/14/2021-22.
                Categories
                Article

                covid-19,intelligent system,machine learning (ml),mathematical model,ml tasks

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