Artificial intelligence and machine learning to fight COVID-19

Alimadadi, Ahmad; Aryal, Sachin; Manandhar, Ishan; Munroe, Patricia B.; Joe, Bina; Cheng, Xi

doi:10.1152/physiolgenomics.00029.2020

Cited by 497 publications

(364 citation statements)

References 8 publications

(9 reference statements)

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“…Although ML methods were used in modeling former pandemics (e.g., Ebola, Cholera, swine fever, H1N1 influenza, dengue fever, Zika, oyster norovirus [11,[39][40][41][42][43][44][45][46][47][48]), there is a gap in the literature for peer-reviewed papers dedicated to COVID-19. Nevertheless, machine learning has been strongly proposed as a great potential for the fight against COVID-19 [49,50]. Machine learning delivered promising results in several aspects for mitigation and prevention and have been endorsed in the scientific community for, e.g., case identifications [51], classification of novel pathogens [52], modification of SIR-based models [53], diagnosis [54,55], survival prediction [56], and ICU demand prediction [57].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

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Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptibleinfected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér

Felde

Mosavi

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Pintér¹,

Felde²,

Mosavi³

et al. 2020

Preprint

View full text Add to dashboard Cite

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

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“…A number of biomedical studies have already applied ML techniques in their work on surveillance, trends, and clinical predictors for the ongoing pandemic (e.g., Alimadadi et al, 2020;Carrillo-Larco & Castillo-Cara, 2020;Ge et al, 2020;Kim et al, 2020;Kumar et al, 2020;Rao and Vazquez, 2020;Yan et al, 2020). Our novel application of ML methods to available coronavirus abstracts, including those about COVID-19, offers insights into the themes of COVID-19 research that overlap with studies about other coronaviruses.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Maps Research Needs in COVID-19 Literature

Doanvo

Qian

Ramjee

et al. 2020

Preprint

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Manually assessing the scope of the thousands of publications on the COVID-19 (coronavirus disease 2019) pandemic is an overwhelming task. Shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of coronavirus abstracts to identify research overlap between COVID-19 and other coronavirus diseases, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggested that COVID-19 studies to date are primarily clinical-, modeling-or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Topic modeling also indicated that COVID-19 publications have thus far focused primarily on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.

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Artificial intelligence and machine learning to fight COVID-19

Cited by 497 publications

References 8 publications

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach

Machine Learning Maps Research Needs in COVID-19 Literature

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