Background <p>Dengue fever is a major mosquito-borne disease that is spreading rapidly from endemic regions to previously low-incidence regions. The regions at the interface between Southeast Asia and China represent critical hotspots for the cross-border and subsequent local transmission of dengue fever. In this study, a cross-population transmission model was constructed to characterize the transmission dynamics in China's border regions adjacent to Myanmar.</p> Methods <p>Dengue case data from the dengue fever case reporting system were collected for Dehong Dai and Jingpo Autonomous Prefecture (DH) and Lincang City (LC), which are situated along the China-Myanmar border in southwestern China (2014–2023). We analyzed spatiotemporal patterns of dengue transmission and developed a transmission dynamics model that accounts for vertical transmission in mosquitoes to quantify transmission dynamics at three levels: the overall disease system, mosquito-to-human, and human-to-mosquito. Model parameters were estimated using least-squares fitting to observed case data, and model performance was evaluated using the coefficient of determination (<i>R</i><sup>2</sup>).</p> Results <p>From 2014 to 2023, a total of 10,180 dengue cases were documented across two China's border prefectures adjacent to Myanmar, 83.1% of which were locally acquired infections. There were 7,893 cases reported in DH (906 international imports, 6,961 local) and 2,287 cases reported in LC (717 international imports, 1,497 local). Transmission exhibited pronounced seasonality, peaking between July and November, and a strong temporal correlation between imported and local cases was observed. By using a dynamic transmission model incorporating mosquito vertical transmission, we achieved statistically significant model fits (<i>P</i> &lt; 0.001) and quantified temporal changes in transmissibility. During the rising phases of the outbreak, the overall transmissibility consistently exceeded 1. Analysis of directional transmission revealed a marked temporal shift: human-to-mosquito transmissibility was predominant in earlier outbreak years (2017: 1–5 in DH, 10–15 in LC), whereas mosquito-to-human transmissibility has increased substantially in recent years (2023: 5–15 in DH, 10–20 in LC). Sensitivity analyses demonstrated that while overall transmissibility estimates remained robust across different initial mosquito population assumptions, the directional transmission components were sensitive to the ratio of exposed to infected mosquitoes, reflecting the inherent identifiability challenges of the model in the absence of entomological surveillance data.</p> Conclusions <p>Our findings reveal the dengue transmission dynamics in China's border regions adjacent to Myanmar over the 2014–2023 study period. Our results underscore the necessity of integrating entomological monitoring with case-based surveillance and support enhanced cross-border coordination for effective outbreak prevention in high-risk frontier zones.</p> Graphical Abstract <p></p>

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Dengue transmission dynamics in China’s border regions adjacent to Myanmar

  • Yanshu Ke,
  • Hongjie Wei,
  • Chunhai Luo,
  • Jiahui Li,
  • Fang Xie,
  • Bingbing Wang,
  • Hong Liu,
  • Hongning Zhou,
  • Qiuping Chen,
  • Jia Rui,
  • Tianmu Chen

摘要

Background

Dengue fever is a major mosquito-borne disease that is spreading rapidly from endemic regions to previously low-incidence regions. The regions at the interface between Southeast Asia and China represent critical hotspots for the cross-border and subsequent local transmission of dengue fever. In this study, a cross-population transmission model was constructed to characterize the transmission dynamics in China's border regions adjacent to Myanmar.

Methods

Dengue case data from the dengue fever case reporting system were collected for Dehong Dai and Jingpo Autonomous Prefecture (DH) and Lincang City (LC), which are situated along the China-Myanmar border in southwestern China (2014–2023). We analyzed spatiotemporal patterns of dengue transmission and developed a transmission dynamics model that accounts for vertical transmission in mosquitoes to quantify transmission dynamics at three levels: the overall disease system, mosquito-to-human, and human-to-mosquito. Model parameters were estimated using least-squares fitting to observed case data, and model performance was evaluated using the coefficient of determination (R2).

Results

From 2014 to 2023, a total of 10,180 dengue cases were documented across two China's border prefectures adjacent to Myanmar, 83.1% of which were locally acquired infections. There were 7,893 cases reported in DH (906 international imports, 6,961 local) and 2,287 cases reported in LC (717 international imports, 1,497 local). Transmission exhibited pronounced seasonality, peaking between July and November, and a strong temporal correlation between imported and local cases was observed. By using a dynamic transmission model incorporating mosquito vertical transmission, we achieved statistically significant model fits (P < 0.001) and quantified temporal changes in transmissibility. During the rising phases of the outbreak, the overall transmissibility consistently exceeded 1. Analysis of directional transmission revealed a marked temporal shift: human-to-mosquito transmissibility was predominant in earlier outbreak years (2017: 1–5 in DH, 10–15 in LC), whereas mosquito-to-human transmissibility has increased substantially in recent years (2023: 5–15 in DH, 10–20 in LC). Sensitivity analyses demonstrated that while overall transmissibility estimates remained robust across different initial mosquito population assumptions, the directional transmission components were sensitive to the ratio of exposed to infected mosquitoes, reflecting the inherent identifiability challenges of the model in the absence of entomological surveillance data.

Conclusions

Our findings reveal the dengue transmission dynamics in China's border regions adjacent to Myanmar over the 2014–2023 study period. Our results underscore the necessity of integrating entomological monitoring with case-based surveillance and support enhanced cross-border coordination for effective outbreak prevention in high-risk frontier zones.

Graphical Abstract