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Machine learning-based diffusion model for prediction of coronavirus-19 outbreak

Authors
  • Raheja, Supriya1
  • Kasturia, Shreya1
  • Cheng, Xiaochun2
  • Kumar, Manoj3
  • 1 Amity University,
  • 2 Middlesex University,
  • 3 University of Petroleum and Energy Studies,
Type
Published Article
Journal
Neural Computing and Applications
Publisher
Springer-Verlag
Publication Date
Aug 12, 2021
Pages
1–20
Identifiers
DOI: 10.1007/s00521-021-06376-x
PMID: 34400853
PMCID: PMC8358916
Source
PubMed Central
Keywords
Disciplines
  • S.I.: IoT-based Health Monitoring System
License
Unknown

Abstract

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

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