Evidence map›Paper›PMID 41947237›Full record

ArticleInfectious diseases of poverty2026

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 and 1 more

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Article in Infectious diseases of poverty, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Yanshu Ke *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Hongjie Wei *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Chunhai Luo *Yunnan Innovative Team of Key Techniques for Vector-Borne Disease Control and Prevention, Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Yunan International Joint Laboratory of Tropical Infectious Diseases, Yunnan International Joint Laboratory of Vector Biology and Control, Yunnan Institute of Parasitic Diseases, Pu'er, 665000, Yunnan, China.
Jiahui LiState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Fang XieState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Bingbing WangState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Hong LiuState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China.
Hongning ZhouYunnan Innovative Team of Key Techniques for Vector-Borne Disease Control and Prevention, Yunnan Provincial Key Laboratory of Vector-Borne Diseases Control and Research, Yunan International Joint Laboratory of Tropical Infectious Diseases, Yunnan International Joint Laboratory of Vector Biology and Control, Yunnan Institute of Parasitic Diseases, Pu'er, 665000, Yunnan, China. zhouhn66@163.com.
Qiuping ChenState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China. chenqp241@xmu.edu.cn.
Jia RuiDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha City, Hunan Province, China. ruijia5345@163.com.
Tianmu ChenState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China. chentianmu@xmu.edu.cn.ORCID http://orcid.org/0000-0003-0710-5086

Funding

Fujian Provincial Health Young and Middle-aged Key Talent Training Program 2024GGA038Furong Laboratory Scientific Research Project 2023SK2090the National Key Research and Development Program of China 2024YFC2311404the National Natural Science Foundation of China 82404329the NHC Key Laboratory of Parasite and Vector Biology National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention NHCKFKT2024-3the Postdoctoral Fellowship Program of China GZC20240890
6 · The paper itself

Abstract

backgroundDengue 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.

methodsDengue 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 (R

resultsFrom 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.

conclusionsOur 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.

Indexed as

DengueAedesAnimalsChinaDengue VirusHumansMosquito VectorsMyanmarDengue feverMathematical modelTansmission dynamicsVector-borne infectious diseases

Identifiers

PMID41947237
PMCPMC13055008

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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.