ArticleTransboundary and emerging diseases2022
A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions.
Article in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Stability analysis and numerical evaluations of a COVID-19 model with vaccination.BMC medical research methodology · 2024Article
- Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data.Frontiers in public health · 2023Article
- A multicompartment mathematical model based on host immunity for dissecting COVID-19 heterogeneity.Heliyon · 2022Article
- A multi-regional, hierarchical-tier mathematical model of the spread and control of COVID-19 epidemics from epicentre to adjacent regions.Transboundary and emerging diseases · 2022Article
- Distinct effectiveness in containing COVID-19 epidemic: Comparative analysis of two cities in China by mathematical modeling.PLOS global public health · 2021Article
- Projecting the global spread of the 2022 monkeypox outbreak considering population mobility.Travel medicine and infectious diseaseArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Epicentres are the focus of COVID-19 research, whereas emerging regions with mainly imported cases due to population movement are often neglected. Classical compartmental models are useful, however, likely oversimplify the complexity when studying epidemics. This study aimed to develop a multi-regional, hierarchical-tier mathematical model for better understanding the complexity and heterogeneity of COVID-19 spread and control. By incorporating the epidemiological and population flow data, we have successfully constructed a multi-regional, hierarchical-tier SLIHR model. With this model, we revealed insight into how COVID-19 was spread from the epicentre Wuhan to other regions in Mainland China based on the large population flow network data. By comprehensive analysis of the effects of different control measures, we identified that Level 1 emergency response, community prevention and application of big data tools significantly correlate with the effectiveness of local epidemic containment across different provinces of China outside the epicentre. In conclusion, our multi-regional, hierarchical-tier SLIHR model revealed insight into how COVID-19 spread from the epicentre Wuhan to other regions of China, and the subsequent control of local epidemics. These findings bear important implications for many other countries and regions to better understand and respond to their local epidemics associated with the ongoing COVID-19 pandemic.
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