Coronavirus disease 2019 (COVID-19): A literature review.

https://journals.physiology.org/doi/pdf/10.1152/physiolgenomics.00029.2020

Artificial intelligence and machine learning to fight COVID-19.

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020 In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment;clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures;and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics ©2020 AI Access Foundation All rights reserved

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Mapping the landscape of artificial intelligence applications against COVID-19

Background: The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools.
Objective: The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.
Methods: We conducted a systematic review of early COVID-19–related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas.
Results: The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)–powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.
Conclusions: In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.

Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature.

COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naive Bayes Model has the highest specificity of 94.30%.

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Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset

The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

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Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

/pdf/machine-learning-using-intrinsic-genomic-signatures-for-4256dtp08f.pdf

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

As of February 3, 2020, the 2019 Novel Coronavirus (2019-nCoV) spread to 27 countries with 362 deaths and more than 17000 confirmed cases. 2019-nCoV is being compared to the infamous SARS coronavirus outbreak. Between November 2002 and July 2003, SARS resulted in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Mainland China alone suffered 349 deaths and 5327 confirmed cases. Though 2019-nCoV has a death rate of 2.2% as of 3 February, the 174895 confirmed cases in a few weeks (December 8, 2019 to February 3, 2020) are alarming. Cases are likely under-reported given the comparatively longer incubation period. Such outbreaks demand rapid elucidation and analysis of the virus genomic sequence for timely treatment plans. We classify the 2019-nCoV using MLDSP and MLDSP-GUI, alignment-free methods that use Machine Learning (ML) and Digital Signal Processing (DSP) for genome analyses. Genomic sequences were mapped into their respective genomic signals (discrete numeric series) using a two-dimensional numerical representation (Chaos Game Representation). The magnitude spectra were computed by applying Discrete Fourier Transform on the genomic signals. The Pearson Correlation Coefficient was used to calculate a pairwise distance matrix. The feature vectors were constructed from the distance matrix and used as an input to the supervised machine learning algorithms. 10-fold cross-validation was applied to compute the average classification accuracy scores. The trained classifier models were used to predict the labels of 29 2019-nCoV sequences. The classification strategy used over 5000 genomes and tested associations at taxonomic levels of realm to species. From our machine learning-based alignment-free analyses using MLDSP-GUI, we corroborate the current hypothesis of a bat origin and classify 2019-nCoV as Sarbecovirus, within Betacoronavirus.

/pdf/machine-learning-based-analysis-of-genomes-suggests-367ss3x41i.pdf

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

As of February 8, 2020, the 2019 Novel Coronavirus (2019-nCoV) spread to 29 countries with 725 deaths and more than 34000 confirmed cases. 2019-nCoV is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though 2019-nCoV has a death rate of 2% as of 8 February, the 34963 confirmed cases in a few weeks (December 8, 2019 to February 8, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper proposes the use of a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole 2019-nCoV genomes. We namely classify the 2019-nCoV using MLDSP and MLDSP-GUI, alignment-free methods that use Machine Learning (ML) and Digital Signal Processing (DSP) for genome analyses. These tools are used to analyze a large dataset of unique viral genomic sequences, totalling 61.8 million bp, with a “decision tree” approach for successive refinements of taxonomic classification. Our results support the hypothesis of a bat origin and classify 2019-nCoV as Sarbecovirus, within Betacoronavirus. We use Spearman’s rank correlation analysis to confirm the relatedness of the 2019-nCoV sequences to the known genera of the family Coronaviridae and the known sub-genera of the genus Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within seconds, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification. Author summary Analyzing over 5000 diverse viral complete genomes, we obtained a 100% accuracy score for classification of 2019-nCoV as Coronaviridae, Betacoronavirus, and finally as belonging to the sub-genus Sarbecovirus, using an alignment-free, supervised machine learning approach. Genomes identified as closely related to 2019-nCoV are bat betacoronaviruses within the same genus, and this supports the hypothesis of a bat origin for this novel coronavirus. This alignment-free analysis of genomic signatures using machine learning requires no prior knowledge of genic or regulatory content, and accurately classifies genomes of unknown taxonomy to potentially genus level resolution within minutes. This suggests that, for novel viral and pathogen genome sequences, such alignment-free machine-learning analyses can provide a reliable real-time option for taxonomic classification.

https://www.biorxiv.org/content/biorxiv/early/2020/02/09/2020.02.03.932350.full.pdf

Hadi El Roz

Papers

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study.

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

Machine learning analysis of genomic signatures provides evidence of associations between Wuhan 2019-nCoV and bat betacoronaviruses