COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.
| Title: | COVID-19 in Iran: Forecasting Pandemic Using Deep Learning. |
|---|---|
| Authors: | Kafieh R; Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Arian R; Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Saeedizadeh N; Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Amini Z; Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Serej ND; Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Minaee S; Snap Inc., Machine Learning Research Team, Seattle, WA, USA.; Yadav SK; Nocturne GmbH, Berlin, Germany.; Vaezi A; Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Rezaei N; Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.; Haghjooy Javanmard S; Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran. |
| Source: | Computational and mathematical methods in medicine [Comput Math Methods Med] 2021 Feb 25; Vol. 2021, pp. 6927985. Date of Electronic Publication: 2021 Feb 25 (Print Publication: 2021). |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Hindawi Country of Publication: United States NLM ID: 101277751 Publication Model: eCollection Cited Medium: Internet ISSN: 1748-6718 (Electronic) Linking ISSN: 1748670X NLM ISO Abbreviation: Comput Math Methods Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2011-2024 : New York : Hindawi; Original Publication: London : Taylor & Francis, c2006- |
| MeSH Terms: | Pandemics*/statistics & numerical data ; Deep Learning* ; SARS-CoV-2*; COVID-19/*epidemiology; Forecasting/methods ; Iran/epidemiology ; Computational Biology ; Databases, Factual ; Humans ; Mathematical Concepts ; Models, Statistical ; Neural Networks, Computer ; Time Factors |
| Abstract: | COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R 2. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R 2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.; (Copyright © 2021 Rahele Kafieh et al.) |
| Competing Interests: | The authors declare that there is no conflict of interest regarding the publication of this paper. |
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| Entry Date(s): | Date Created: 20210308 Date Completed: 20210318 Latest Revision: 20210318 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC7907749 |
| DOI: | 10.1155/2021/6927985 |
| PMID: | 33680071 |
| Database: | MEDLINE |
Journal Article