ArticleBMC public health2023
Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Epidemiological characteristics and transmission dynamics of COVID-19 outbreaks in China: a four-year retrospective analysis using extended SEIR models.BMC public health · 2026Article
- A dual-branch deep learning framework for tiered early warning of COVID-19 utilizing wastewater data.Journal of water and health · 2026Article
- Trends, inequalities and time-series based prediction of knee osteoarthritis attributed to high body-mass-index: findings from global burden of disease 2021.Journal of orthopaedic translation · 2025Article
- A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction.Scientific reports · 2025Article
- Changes in biosafety practices of Brazilian orthodontists after the COVID-19 pandemic: a cross-sectional study.Brazilian oral research · 2025Article
- Research on safety strategies for nucleic acid testing in sudden epidemic conditions based on a SEIARD dynamic model.Scientific reports · 2024Article
- Assessing the impact of vaccination and medical resource allocation on infectious disease outbreak management: a case study of COVID-19 in Taiyuan City.Frontiers in public health · 2024Article
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9 authors.
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Abstract
backgroundIn 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.
methodsGiven the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.
resultsThe best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to R
conclusionsThe LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.
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