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Cited by 105COVID-19 epidemiological studiesStock Market Forecasting MethodsCOVID-19 diagnosis using AI

Forecasting covid-19 new cases using deep learning methods

Open access
Xu, Lu, Magar, Rishikesh, Barati Farimani, Amir·2022·Computers in Biology and Medicine
DOI 10.1016/j.compbiomed.2022.105342PMC PMC8864960
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Abstract

After nearly two years since the first identification of SARS-CoV-2 virus, the surge in cases because of virus mutations is a cause of grave public health concern across the globe. As a result of this health crisis, predicting the transmission pattern of the virus is one of the most vital tasks for preparing and controlling the pandemic. In addition to mathematical models, machine learning tools, especially deep learning models have been developed for forecasting the trend of the number of patients affected by SARS-CoV-2 with great success. In this paper, three deep learning models, including CNN, LSTM, and the CNN-LSTM have been developed to predict the number of COVID-19 cases for Brazil, India and Russia. We also compare the performance of our models with the previously developed deep learning models and notice significant improvements in prediction performance. Although our models have been used only for forecasting cases in these three countries, the models can be easily applied to datasets of other countries. Among the models developed in this work, the LSTM model has the highest performance when forecasting and shows an improvement in the forecasting accuracy compared with some existing models. The research will enable accurate forecasting of the COVID-19 cases and support the global fight against the pandemic.

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  • Deep learning algorithms have shown remarkable success in medical image analysis due to their ability to learn complex features from limited data.

    “Deep learning algorithms, on the other hand, have shown remarkable success in these tasks due to their ability to learn complex features from limited data [12 -14].”

    From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [13]

    The Related work section mentions successful pandemic applications using medical images, including chest X-rays. Methods > CNN says CNNs can extract features automatically, but discusses that capability for time-series forecasting, not medical image analysis. The paper does not establish that success in medical image analysis results from learning complex features from limited data. No page numbers are available in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • End-to-end convolutional neural networks have dominated performance at diverse medical imaging tasks.

    “Motivated by the dominant performance of CNNs, it is desirable to develop an end-to-end network for automatic placental maturity grading (PMG) from ultrasound images.”

    From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [13]

    The Methods > CNN passage discusses automated feature extraction and time-series forecasting, not medical imaging. The Results and discussion > Comparison of models passage compares CNNs with LSTM-based models for COVID-19 case forecasting. Searches for medical imaging, ultrasound, end-to-end networks, and dominant CNN performance found no passage addressing the attributed claim. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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5 of the 34 references couldn’t be matched to a known paper.

Research works printed but not linked: 1

  1. [27]Nair V. Hinton G.E. Rectified Linear Units Improve Restricted Boltzmann Machines vol. 8 2010

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Authors
Xu, L., Magar, R., & Barati Farimani, A.
Affiliations
Carnegie Mellon University (US)
ORCID
Venue
Computers in Biology and Medicine
Publisher
Elsevier BV
Published
2022-05
Volume
144
Pages
105342
ISSN
0010-4825 (ISSN-L)
DOI
10.1016/j.compbiomed.2022.105342
PMC
PMC8864960
OpenAlex
W4213109153
Cited by
105
References
34
Citations by year
2026: 11 · 2025: 18 · 2024: 26 · 2023: 38 · 2022: 12
FWCI
13.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Biology and Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Keywords
COVID-19 pandemic, SARS-CoV-2, case prediction, epidemic forecasting, LSTM, convolutional neural networks, CNN-LSTM, deep learning, time series forecasting, Brazil, India, Russia
MeSH terms
Deep Learning, COVID-19, SARS-CoV-2, Forecasting, Humans, Pandemics

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