Isfahan and Covid-19: Deep spatiotemporal representation.
| Title: | Isfahan and Covid-19: Deep spatiotemporal representation. |
|---|---|
| Authors: | Kafieh 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.; Arian R; 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.; Vaezi A; Department of Community and Family Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.; Javanmard SH; Applied physiology research center, Isfahan cardiovascular research institute, Isfahan university of medical sciences, Isfahan, Iran. |
| Source: | Chaos, solitons, and fractals [Chaos Solitons Fractals] 2020 Dec; Vol. 141, pp. 110339. Date of Electronic Publication: 2020 Oct 05. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Pergamon Press Country of Publication: England NLM ID: 100971564 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0960-0779 (Print) Linking ISSN: 09600779 NLM ISO Abbreviation: Chaos Solitons Fractals Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Oxford ; New York : Pergamon Press, c1991- |
| Abstract: | The coronavirus COVID-19 is affecting 213 countries and territories around the world. Iran was one of the first affected countries by this virus. Isfahan, as the third most populated province of Iran, experienced a noticeable epidemic. The prediction of epidemic size, peak value, and peak time can help policymakers in correct decisions. In this study, deep learning is selected as a powerful tool for forecasting this epidemic in Isfahan. A combination of effective Social Determinant of Health (SDH) and the occurrences of COVID-19 data are used as spatiotemporal input by using time-series information from different locations. Different models are utilized, and the best performance is found to be for a tailored type of long short-term memory (LSTM). This new method incorporates the mutual effect of all classes (confirmed/ death / recovered) in the prediction process. The future trajectory of the outbreak in Isfahan is forecasted with the proposed model. The paper demonstrates the positive effect of adding SDHs in pandemic prediction. Furthermore, the effectiveness of different SDHs is discussed, and the most effective terms are introduced. The method expresses high ability in both short- and long- term forecasting of the outbreak. The model proves that in predicting one class (like the number of confirmed cases), the effect of other accompanying numbers (like death and recovered cases) cannot be ignored. In conclusion, the superiorities of this model (particularity the long term predication ability) turn it into a reliable tool for helping the health decision-makers.; (© 2020 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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| Contributed Indexing: | Keywords: COVID-19; Deep learning; Isfahan; Predication |
| Entry Date(s): | Date Created: 20201012 Latest Revision: 20231112 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC7534756 |
| DOI: | 10.1016/j.chaos.2020.110339 |
| PMID: | 33041534 |
| Database: | MEDLINE |
Journal Article