ArticlePloS one2020
Predicting and analyzing the COVID-19 epidemic in China: Based on SEIRD, LSTM and GWR models.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of spatial epidemiological modeling approaches applied during the COVID-19 pandemic.BMC public health · 2026Pooled it
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.medRxiv : the preprint server for health sciences · 2025Article
- Article
- MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data.Scientific reports · 2024Article
- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- COVID-19 pandemic and minority health disparities in New York City: A spatial and temporal perspective.Environment and planning. B, urban analytics and city science · 2023Article
- A Hybrid Model for Coronavirus Disease 2019 Forecasting Based on Ensemble Empirical Mode Decomposition and Deep Learning.International journal of environmental research and public health · 2022Article
- Spatial differentiation and determinants of COVID-19 in Indonesia.BMC public health · 2022Article
- BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting.Scientific reports · 2022Article
- Multiscale heterogeneous optimal lockdown control for COVID-19 using geographic information.Scientific reports · 2022Article
- Article
- Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review.Frontiers in public health · 2022Review
- The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington.Sustainable cities and society · 2021Article
- A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.Transactions in GIS : TG · 2021Review
- Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China.Environmental science and pollution research international · 2021Article
- Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan.PloS one · 2021Article
- Control strategies for COVID-19 epidemic with vaccination, shield immunity and quarantine: A metric temporal logic approach.PloS one · 2021Article
- Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States.IEEE open journal of engineering in medicine and biology · 2021Article
- Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models.Computational urban science · 2021Article
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12 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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