tetano
Editor, Senior Moderator
PLoS One
. 2020 Aug 27;15(8):e0238280.
doi: 10.1371/journal.pone.0238280. eCollection 2020.
Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models
Fenglin Liu[SUP] 1 [/SUP], Jie Wang[SUP] 1 [/SUP], Jiawen Liu[SUP] 1 [/SUP], Yue Li[SUP] 1 [/SUP], Dagong Liu[SUP] 1 [/SUP], Junliang Tong[SUP] 1 [/SUP], Zhuoqun Li[SUP] 1 [/SUP], Dan Yu[SUP] 1 [/SUP], Yifan Fan[SUP] 1 [/SUP], Xiaohui Bi[SUP] 1 [/SUP], Xueting Zhang[SUP] 1 [/SUP], Steven Mo[SUP] 1 [/SUP]
Affiliations
Abstract
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.
. 2020 Aug 27;15(8):e0238280.
doi: 10.1371/journal.pone.0238280. eCollection 2020.
Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models
Fenglin Liu[SUP] 1 [/SUP], Jie Wang[SUP] 1 [/SUP], Jiawen Liu[SUP] 1 [/SUP], Yue Li[SUP] 1 [/SUP], Dagong Liu[SUP] 1 [/SUP], Junliang Tong[SUP] 1 [/SUP], Zhuoqun Li[SUP] 1 [/SUP], Dan Yu[SUP] 1 [/SUP], Yifan Fan[SUP] 1 [/SUP], Xiaohui Bi[SUP] 1 [/SUP], Xueting Zhang[SUP] 1 [/SUP], Steven Mo[SUP] 1 [/SUP]
Affiliations
- PMID: 32853285
- DOI: 10.1371/journal.pone.0238280
Abstract
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.