Evidence map›Paper›PMID 40632736›Full record

ArticlePLoS neglected tropical diseases2025

Forecasting high-risk areas for dengue outbreaks in China: A trend analysis of Aedes albopictus and Aedes aegypti distributions from 2014 to 2030.

Yulun Xie, Bingbing Wang, Qi Chen, Hongjie Wei, Yanshu Ke, Fang Xie, Xuhua Guan, Jia Rui, Tianmu Chen

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Detection of insecticide resistance and knockdown resistance (Frontiers in cellular and infection microbiology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yulun 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, People's Republic of 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, People's Republic of China.
Qi ChenHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Hongjie WeiState 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, People's Republic of China.
Yanshu KeState 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, People's Republic of 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, People's Republic of China.
Xuhua GuanHubei Provincial Center for Disease Control and Prevention, Wuhan, China.
Jia RuiState 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, People's Republic of China.
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, People's Republic of China.ORCID 0000-0003-0710-5086

Funding

Fujian Provincial Department of Science and Technology ProgramHubei Province Public Health Leading Talent ProgramNational Natural Science Foundation of ChinaNHC Key Laboratory of Parasite and Vector BiologyPostdoctoral Fellowship Program of ChinaXiang'an Innovation Laboratory/National Key Laboratory for Infectious Disease Vaccine Development Science and Technology Project
6 · The paper itself

Abstract

backgroundGlobal warming, urbanization and the resumption of global population movements post-COVID-19 have made the prevention and control of dengue and its vector transmission more challenging. To tackle this issue, this study evaluated and predicted the suitable habitats of Aedes albopictus and Aedes aegypti in China. This study aims to identify the key influencing factors, analyze patterns of habitat expansion and contraction, and explore regions in China at risk of dengue fever outbreaks in the future. METHODOLOGY/PRINCIPAL

findingsThis study utilized mosquito distribution data from 2010 to 2023 and employed Maxent to map the distribution of suitable habitats. The key influencing factors and response curves were further analysed, and patterns of habitat expansion and contraction were investigated. The findings reveal that the main variables affecting the distributions of Ae. albopictus and Ae. aegypti are annual precipitation, annual mean temperature, and land use and land cover changes. The suitable habitat for Ae. albopictus shows a significant northward expansion trend, reaching large areas in Shandong and Henan province by 2030. The suitable habitat area for Ae. albopictus is increasing annually and can reach approximately 2.38 million square kilometers by 2030. Compared to the outbreak year of dengue fever in China in 2019, the suitable habitat area for Ae. albopictus in 2030 will increase by approximately 17.06%, with a growth of 2.57% in the sum of high-risk and medium-risk suitable habitat areas. In contrast, the suitable habitats of Ae. aegypti are primarily concentrated in Guangdong, Hainan and Yunnan Provinces. CONCLUSION/SIGNIFICANCE: This study compared the potential changes in the distributions of suitable habitats for Ae. albopictus and Ae. aegypti in 2014, 2019, and 2023 and predicted suitable habitats for 2030, as well as contraction and expansion trends in the suitable habitats of Ae. albopictus. The findings aim to identify regions at risk of future dengue fever outbreaks in China, providing a scientific basis for public health authorities to develop effective dengue prevention and control strategies.

Indexed as

AedesAnimal DistributionDengueDisease OutbreaksMosquito VectorsAnimalsChinaEcosystemForecastingHumansPattern Analysis, Machine

Identifiers

PMID40632736
PMCPMC12240387

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.