ArticleJournal of thoracic disease2020
Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions.
Article in Journal of thoracic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 521 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
521 citing papers in PubMed, 3 syntheses or guidelines pooled it, 1,494 citations in OpenAlex.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Data-driven strategies for model-informed decision-making during the COVID-19 pandemic: a systematic review.BMJ open · 2026Pooled it
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Early Immunological Biomarkers for Personalized Treatment Selection in Severe COVID-19: Post Hoc Machine Learning Analysis of a Randomized Clinical Trial.JMIR medical informatics · 2026Article
- Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China.Infectious Disease Modelling · 2026Article
- Mechanistic Understanding of Pandemic Dynamics: A Multiscale Algorithmic Framework.Life (Basel, Switzerland) · 2026Article
- Inference of latent epidemic regimes and generative simulations reveal how inequality and mobility shape COVID-19 transmission.Scientific reports · 2026Article
- Modeling the COVID-19 epidemic in kurdistan province using the SEIR model and time series analysis of hospitalized patients during the first wave.BMC infectious diseases · 2026Observational
- A COVID-19 prediction Model based on symptomatology google trends and its optimization.BMC infectious diseases · 2026Article
- Integrating epidemiological modeling and time-series forecasting to optimize pandemic patient allocation.International journal of health geographics · 2026Article
- Real-time dynamic prediction of HFMD transmission using SEIRQ-ARIMA hybrid model optimized by multi-stage ABC-GWO algorithm.Scientific reports · 2026Article
- Transportation control and epidemic containment: empirical evidence from China's Wuhan lockdown.Frontiers in public health · 2026Article
- Epidemic dynamics prediction using fractional SIRD and deep learning.Scientific reports · 2025Article
- Flexible school attendance strategies for epidemic outbreaks: simulation-based decision support.BMC public health · 2025Article
- A spatiotemporal transmission simulator for respiratory infectious diseases and its application to COVID-19.Infectious Disease Modelling · 2025Article
- Traffic-driven spatio-temporal prediction for fine-scale epidemic outbreaks in China.BMC infectious diseases · 2025Article
- An Optimization-Based Framework to Dynamically Schedule Hospital Beds in a Pandemic.Healthcare (Basel, Switzerland) · 2025Article
- Global infectious disease early warning models: An updated review and lessons from the COVID-19 pandemic.Infectious Disease Modelling · 2025Review
- Neural networks to model COVID-19 dynamics and allocate healthcare resources.Scientific reports · 2025Article
- A machine learning approach for diagnostic and prognostic predictions, key risk factors and interactions.Health services & outcomes research methodology · 2025Article
461 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Commented on by
- Commented on by
- Commented on by
Authors and funding
26 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe coronavirus disease 2019 (COVID-19) outbreak originating in Wuhan, Hubei province, China, coincided with
methodsWe integrated population migration data before and after January 23 and most updated COVID-19 epidemiological data into the Susceptible-Exposed-Infectious-Removed (SEIR) model to derive the epidemic curve. We also used an artificial intelligence (AI) approach, trained on the 2003 SARS data, to predict the epidemic.
resultsWe found that the epidemic of China should peak by late February, showing gradual decline by end of April. A five-day delay in implementation would have increased epidemic size in mainland China three-fold. Lifting the Hubei quarantine would lead to a second epidemic peak in Hubei province in mid-March and extend the epidemic to late April, a result corroborated by the machine learning prediction.
conclusionsOur dynamic SEIR model was effective in predicting the COVID-19 epidemic peaks and sizes. The implementation of control measures on January 23 2020 was indispensable in reducing the eventual COVID-19 epidemic size.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.