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COVID-19 Outbreak Prediction with Machine Learning

EasyChair Preprint no. 3193

38 pagesDate: April 18, 2020


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.

Keyphrases: Coronavirus, coronavirus disease, COVID-19, machine learning, model, prediction, SARS-CoV-2

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sina F. Ardabili and Amir Mosavi and Pedram Ghamisi and Filip Ferdinand and Annamaria R. Varkonyi-Koczy and Uwe Reuter and Timon Rabczuk and Peter M. Atkinson},
  title = {COVID-19 Outbreak Prediction with Machine Learning},
  howpublished = {EasyChair Preprint no. 3193},

  year = {EasyChair, 2020}}
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