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1000 Titel
  • Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models
1000 Autor/in
  1. Liu, Fenglin |
  2. Wang, Jie |
  3. Liu, Jiawen |
  4. Li, Yue |
  5. Liu, Dagong |
  6. Tong, Junliang |
  7. Li, Zhuoqun |
  8. Yu, Dan |
  9. Fan, Yifan |
  10. Bi, Xiaohui |
  11. Zhang, Xueting |
  12. Mo, Steven |
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-08-27
1000 Erschienen in
1000 Quellenangabe
  • 15(8):e0238280
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0238280 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7451659/ |
1000 Ergänzendes Material
  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238280#sec016 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • In December 2019, the novel coronavirus pneumonia (COVID-19) occurred in Wuhan, Hubei Province, China. The epidemic quickly broke out and spread throughout the country. Now it becomes a pandemic that affects the whole world. In this study, three models were used to fit and predict the epidemic situation in China: a modified SEIRD (Susceptible-Exposed-Infected-Recovered-Dead) dynamic model, a neural network method LSTM (Long Short-Term Memory), and a GWR (Geographically Weighted Regression) model reflecting spatial heterogeneity. Overall, all the three models performed well with great accuracy. The dynamic SEIRD prediction APE (absolute percent error) of China had been ≤ 1.0% since Mid-February. The LSTM model showed comparable accuracy. The GWR model took into account the influence of geographical differences, with R2 = 99.98% in fitting and 97.95% in prediction. Wilcoxon test showed that none of the three models outperformed the other two at the significance level of 0.05. The parametric analysis of the infectious rate and recovery rate demonstrated that China's national policies had effectively slowed down the spread of the epidemic. Furthermore, the models in this study provided a wide range of implications for other countries to predict the short-term and long-term trend of COVID-19, and to evaluate the intensity and effect of their interventions.
1000 Sacherschließung
lokal Epidemiology
gnd 1206347392 COVID-19
lokal Infectious disease epidemiology
lokal Italy
lokal Spatial epidemiology
lokal China
lokal SARS
lokal Death rates
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/TGl1LCBGZW5nbGlu|https://frl.publisso.de/adhoc/uri/V2FuZywgSmll|https://frl.publisso.de/adhoc/uri/TGl1LCBKaWF3ZW4=|https://frl.publisso.de/adhoc/uri/TGksIFl1ZQ==|https://frl.publisso.de/adhoc/uri/TGl1LCBEYWdvbmc=|https://frl.publisso.de/adhoc/uri/VG9uZywgSnVubGlhbmc=|https://frl.publisso.de/adhoc/uri/TGksIFpodW9xdW4=|https://frl.publisso.de/adhoc/uri/WXUsIERhbg==|https://frl.publisso.de/adhoc/uri/RmFuLCBZaWZhbg==|https://frl.publisso.de/adhoc/uri/QmksIFhpYW9odWk=|https://frl.publisso.de/adhoc/uri/WmhhbmcsIFh1ZXRpbmc=|https://frl.publisso.de/adhoc/uri/TW8sIFN0ZXZlbg==
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  1. Taikang Pension & Insurance Co., Ltd. |
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    1000 Förderer Taikang Pension & Insurance Co., Ltd. |
    1000 Förderprogramm -
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1000 Objektart article
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1000 Erstellt am 2020-12-22T14:43:16.824+0100
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1000 Zuletzt bearbeitet Tue Dec 22 14:45:11 CET 2020
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