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Background <p>In 2024, the global new crown epidemic is still not optimistic and the overall situation remains complex and challenging. A detailed and comprehensive analysis of the epidemiological characteristics and transmission dynamics of the COVID-19 epidemic over the past four years is urgently needed.</p> Methods <p>Taiyuan is the political, economic, cultural and international exchange center of central China, with its core driving the rapid development of the central city cluster. Initially, we investigated the epidemiological characteristics of infected cases in Taiyuan. Subsequently, we developed the SSqEEqIAHR, SEIQHR and SEIAHR models to analyze the multi-stage infection trends of COVID-19. Finally, we performed several sensitivity analyses to investigate the effects of model parameters on the basic reproduction number (<i>R</i><sub>0</sub>) and outbreak trends.</p> Results <p>From the perspective of sociodemographic characteristics, a total of 4,935,795 positive infected cases had been reported in Taiyuan. Among them, the vast majority were mild, and the male-to-female ratio was 1.07. The most affected by the epidemic were people aged 60–69 and retirees, with Poland and Russia emerging as primary sources of imported cases. From the perspective of spatiotemporal characteristics, there have been three rounds of epidemic peaks in Taiyuan over the past four years, with Xinghualing, Xiaodian and Yingze being the most heavily infected districts. From the perspective of transmission dynamics, the extended SEIR models can achieve satisfactory prediction results in the three major outbreaks, with goodness-of-fit of 0.734, 0.991 and 0.914, respectively. The <i>R</i><sub>0</sub> values decreased sequentially, with the outbreaks completely dying out after six, 47 and 153 days, respectively. Sensitivity analyses showed that decreasing the transmission rate and increasing the quarantine rate were essential for controlling the spread of the virus.</p> Conclusions <p>The extended SEIR models played an important role in capturing the transmission pattern of multi-stage and multi-wave epidemics. Future interventions should be targeted align with the epidemiological characteristics and transmission dynamics of the outbreak.</p>

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Epidemiological characteristics and transmission dynamics of COVID-19 outbreaks in China: a four-year retrospective analysis using extended SEIR models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Yifei Ma,
  • Yifei Ning,
  • Jiaming Guo,
  • Shujun Xu,
  • Yuxin Luo,
  • Jiantao Li,
  • Lijian Lei,
  • Lu He,
  • Tong Wang,
  • Hongmei Yu,
  • Jun Xie

摘要

Background

In 2024, the global new crown epidemic is still not optimistic and the overall situation remains complex and challenging. A detailed and comprehensive analysis of the epidemiological characteristics and transmission dynamics of the COVID-19 epidemic over the past four years is urgently needed.

Methods

Taiyuan is the political, economic, cultural and international exchange center of central China, with its core driving the rapid development of the central city cluster. Initially, we investigated the epidemiological characteristics of infected cases in Taiyuan. Subsequently, we developed the SSqEEqIAHR, SEIQHR and SEIAHR models to analyze the multi-stage infection trends of COVID-19. Finally, we performed several sensitivity analyses to investigate the effects of model parameters on the basic reproduction number (R0) and outbreak trends.

Results

From the perspective of sociodemographic characteristics, a total of 4,935,795 positive infected cases had been reported in Taiyuan. Among them, the vast majority were mild, and the male-to-female ratio was 1.07. The most affected by the epidemic were people aged 60–69 and retirees, with Poland and Russia emerging as primary sources of imported cases. From the perspective of spatiotemporal characteristics, there have been three rounds of epidemic peaks in Taiyuan over the past four years, with Xinghualing, Xiaodian and Yingze being the most heavily infected districts. From the perspective of transmission dynamics, the extended SEIR models can achieve satisfactory prediction results in the three major outbreaks, with goodness-of-fit of 0.734, 0.991 and 0.914, respectively. The R0 values decreased sequentially, with the outbreaks completely dying out after six, 47 and 153 days, respectively. Sensitivity analyses showed that decreasing the transmission rate and increasing the quarantine rate were essential for controlling the spread of the virus.

Conclusions

The extended SEIR models played an important role in capturing the transmission pattern of multi-stage and multi-wave epidemics. Future interventions should be targeted align with the epidemiological characteristics and transmission dynamics of the outbreak.