Evidence map›Paper›PMID 42069830›Full record

ArticleCommunications medicine2026

Climate and socioeconomic factors drive heterogeneous dengue risk escalation in the Chinese population.

Xu Guang, Yifei He, Mengjie Geng, Meifang Liu, Jia Wan, Dongfeng Kong, Zhen Zhang, Lanbin Xiang, Liangqiang Lin, Rongxin He and 9 more

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

19 authors.

Xu Guang *School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Yifei He *School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Mengjie Geng *Chinese Center for Disease Control and Prevention, Beijing, China.
Meifang LiuSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Jia WanShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Dongfeng KongShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Zhen ZhangShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Lanbin XiangShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Liangqiang LinShenzhen Center for Disease Control and Prevention, Shenzhen, China.
Rongxin HeSchool of Health Management, Southern Medical University, Guangzhou, China.
Ning ZhangVanke School of Public Health, Tsinghua University, Beijing, China.
Felipe Arley Costa PessoaInstituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil.
Claudia Maria Ríos VelasquezInstituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil.
Carolina Mercedes Laurent SinghInstituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil.ORCID http://orcid.org/0000-0002-6013-4647
Pritesh LalwaniInstituto Leoônidas e Maria Deane, Fiocruz Amazoônia, Manaus, Brazil.
Jie HuangSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.ORCID http://orcid.org/0000-0002-9036-4304
Haidong WangSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Jue LiuSchool of Public Health, Peking University, Haidian District, Beijing, China. jueliu@bjmu.edu.cn.ORCID http://orcid.org/0000-0002-1938-9365
Bin ZhuSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China. zhub6@sustech.edu.cn.ORCID http://orcid.org/0000-0002-0091-6356

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDengue risk is increasingly shaped by climate change and rapid urbanization, yet comprehensive, multidimensional risk assessments grounded in a One Health perspective remain scare.

methodsWe develop a geographically eXplainable artificial intelligence (GeoXAI) model to estimate dengue hazard across China in 2024 (current), 2050, and 2100 under different shared socioeconomic pathway (SSP) scenarios. A hazard-exposure-vulnerability framework is then used to assess dengue risk by integrating dengue hazard, human exposure, and social vulnerability.

resultsHere we show a northward expansion of high-hazard areas, with the minimum temperature in the coldest month being the dominant driver (27.2% contribution). Moreover, socioeconomic factors such as population density (3.8%) and urbanization level (2.7%) will further amplify dengue hazard. Current dengue risk assessments reveal high-risk clusters in Southwest China and megacities. Dengue risk exhibits spatially heterogeneous escalation in the future, with Southwest and Southeast China facing the steepest growth and Northwest China experiencing disproportionate increases. Compared to the current, dengue risk in SSP585-a high greenhouse gas emission scenario and limited climate policy interventions-increases by 6.01% (2050) and 8.21% (2100), representing the largest escalation among the three SSPs.

conclusionsDespite ongoing disease control efforts, our findings underscore the need to intensify integrated surveillance and multidimensional intervention strategies against escalating dengue risk in China, and offers lessons for other prevalent Aedes-borne diseases (e.g., chikungunya).

Identifiers

PMID42069830
PMCPMC13342516

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Registered trials

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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.