Open AccessPredicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models
Author(s) -
Fenglin Liu,
Jie Wang,
Jiawen Liu,
Yue Li,
Dagong Liu,
Junliang Tong,
Zhuoqun Li,
Dan Yu,
Yifan Fan,
Xinhui Bi,
Xueting Zhang,
Steven Mo
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0238280
Subject(s) - covid-19 , china , wilcoxon signed rank test , pandemic , demography , statistics , long short term memory , predictive modelling , geography , econometrics , computer science , artificial neural network , medicine , outbreak , mathematics , artificial intelligence , virology , infectious disease (medical specialty) , recurrent neural network , disease , archaeology , mann–whitney u test , pathology , sociology
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 R 2 = 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.