Artificial intelligence and COVID-19: A multidisciplinary approach

Ahuja, Abhimanyu S.; Reddy, Vineet Pasam; Marques, Oge

doi:10.1016/j.imr.2020.100434

Cited by 101 publications

(87 citation statements)

References 2 publications

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“…The application of a machine learning algorithm to sift through trillions of compounds of the vaccine adjuvants can shorten the vaccine development time. The machine learning algorithm can be used for screening compounds for a potential adjuvant candidate for the SARS-CoV-2 vaccine (Ahuja, Reddy & Marques, 2020).…”

Section: Covid-19 Vaccine Developmentmentioning

confidence: 99%

“…The allocation of limited resources in a rapidly expanding pandemic entails a difficult decision for the distribution of scarce resources (Jiang et al, 2020). The epicenters of the COVID-19 are challenged with resource problems of shortage of beds, gowns, masks, medical staff, and ventilators (Ahuja, Reddy & Marques, 2020). We propose the development of a machine learning decision support system to help in crucial decisions on resource allocation.…”

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

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Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Chiroma

Ezugwu

Jauro

et al. 2020

PeerJ Computer Science

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Background and Objective The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. Methods We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

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Section: Covid-19 Vaccine Developmentmentioning

confidence: 99%

Section: Challenges and Future Research Opportunitiesmentioning

confidence: 99%

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Chiroma

Ezugwu

Jauro

et al. 2020

PeerJ Computer Science

View full text Add to dashboard Cite

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“…The joint research efforts in understanding SARS-CoV-2 gives hope that similar efforts will be put together to combat other illnesses such as cancer and neurodegenerative disorders. It has also allowed us to realize that what is seen with SARS-CoV-2 is a form of nanotoxicity and that several new tools such as artificial intelligence have been deployed to understand the disease and develop therapies to combat it (Ahuja et al, 2020;Shaker et al, 2020). Therefore, similar tools should be used in the design of future nanotoxicology studies but also in the development of bioinspired drug delivery systems like viruses (Aulicino et al, 2020).…”

Section: Toxicity Of Covid-19 On Different Cells and The Need For Relmentioning

confidence: 99%

Nanomedicine & Nanotoxicology Future Could Be Reshaped Post-COVID-19 Pandemic

Al-Ahmady

Ali‐Boucetta

2020

Front. Nanotechnol.

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Since its first emergence in December 2019, the coronavirus-2 infection has quickly spread around the world and the severity of the pandemic has already re-shaped our lives. This review highlights the role of nanotechnology in the fight against this pandemic with a focus on the design of effective nano-based prevention and treatment options that overcome the limitations associated with conventional vaccines and other therapies. How nanotechnology could be utilized to understand the pathology of the ongoing pandemic is also discussed as well as how our knowledge about SARS-CoV-2 cellular uptake and toxicity could influence future nanotoxicological considerations and nanomedicine design of safe yet effective nanomaterials.

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“…Antiviral drugs like remdesivir, favipiravir, umifenovir, oseltamivir, lopinavir/ ritonavir and anti-malarial drugs chloroquine and hydroxychloroquine have shown fruitful results in treating COVID-19 patients. The modern upcoming tools of artificial intelligence (AI) and CRISPR are also being utilized to design effective vaccines and therapeutics against SARS-CoV-2 75,76 . The fields of bioinformatics, cheminformatics, structure-based drug development, network-based techniques predicting drug-target interactions, AI and machine learning could aid for designing anti-SARS-CoV-2 drugs 77 .…”

Section: Developing Vaccines Drugs and A Global Cure To Fight Covid-mentioning

confidence: 99%

SARS-CoV-2 / COVID-19: Salient Facts and Strategies to Combat Ongoing Pandemic

Sah¹,

Pokhrel²,

Fathah³

et al. 2020

J Pure Appl Microbiol

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Severe acute respiratory syndrome coronavirus – 2 (SARS-CoV-2), an emerging novel coronavirus causing coronavirus disease 2019 (COVID-19) pandemic, has now rapidly spread to more than 215 countries and has killed nearly 0.75 million people out of more than 20 million confirmed cases as of 10th August, 2020. Apart from affecting respiratory system, the virus has shown multiple manifestations with neurological affections and damaging kidneys. SARS-CoV-2 transmission mainly occurs through close contact of COVID-19 affected person, however air-borne route is also now considered as dominant route of virus spread. The virus has been implicated to have originated from animals. Apart from bats, pangolins and others being investigates to play role in transmitting SARS-CoV-2 as intermediate hosts, the recent reports of this virus infection in other animals (cats, dogs, tigers, lions, mink) suggest one health approach implementation along with adopting appropriate mitigation strategies. Researchers are pacing to develop effective vaccines and drugs, few reached to clinical trials also, however these may take time to reach the mass population, and so till then adopting appropriate prevention and control is the best option to avoid SARS-CoV-2 infection. This article presents an overview on this pandemic virus and the disease it causes, with few recent concepts and advances.

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Artificial intelligence and COVID-19: A multidisciplinary approach

Cited by 101 publications

References 2 publications

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Nanomedicine & Nanotoxicology Future Could Be Reshaped Post-COVID-19 Pandemic

SARS-CoV-2 / COVID-19: Salient Facts and Strategies to Combat Ongoing Pandemic

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