COVID-19 Outbreak Prediction with Machine Learning

By Sina Ardabili, Amir Mosavi, Pedram Ghamisi, F. Ferdinand, Annamária R. Várkonyi-Kóczy, Uwe Reuter, Timon Rabczuk, Peter M. Atkinson. medRxiv, 2020

438 citations recorded by the source corpus.

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly 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.

Subfields: Artificial Intelligence, Modeling and Simulation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e917bdbfa369fed8bcf0972df09b0af05241b9b1

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, background): “Global Supporting Health systems/resource managers, data services Ardabili et al [95] 81”
Machine Learning: Algorithms, Real-World Applications and Research Directions (2021, background): “It can also be used to better understand the virus’s origin, COVID-19 outbreak prediction, as well as for disease diagnosis and treatment [14, 50].”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “This would enable them to implement appropriate control measures (Ardabili et al., 2020) and develop protocols for COVID-19 containment, detection, and prediction.”
Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM (2020, neutral): “Other models are multilayer perceptron (MLP) and adaptive network-based fuzzy inference system, (ANFIS) utilized for evaluating the complex variation behavior and predicting the COVID-19 transmission [18] .”