Evidence map›Paper›PMID 41094355›Full record

ArticleBMC infectious diseases2025

Transmission components of hand, foot, and mouth disease in Guangdong Province and their association with meteorological factors: a modelling study.

Hui Tang, Xudan Chen, Yueqian Wu, Jie Sun, Yikun Chang, Jinwei Chen, Junxi Li, Yongkang Lin, Yiguo Liufu, Danyang Song and 8 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

18 authors.

Hui Tang *Department of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Xudan Chen *Department of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Yueqian Wu *Department of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Jie SunDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Yikun ChangDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Jinwei ChenDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Junxi LiDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Yongkang LinDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Yiguo LiufuDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Danyang SongDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Xinru LuoDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Kexing ZhangDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Yu WangDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Qilan WenDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Lei ZhangDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China.
Wangjian ZhangDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China. zhangwj227@mail.sysu.edu.cn.
Zhicheng DuDepartment of Medical Statistics, School of Public Health & Research Center of Health Informatics & Sun Yat-sen Global Health Institute, Sun Yat-sen University, Guangzhou, China. duzhch5@mail.sysu.edu.cn.
Yuantao HaoPeking University Center for Public Health and Epidemic Preparedness & Response, Peking, China. haoyt@bjmu.edu.cn.

Funding

National Natural Science Foundation of China 82103947Science and Technology Program of Guangzhou, China 2024A04J4623
6 · The paper itself

Abstract

backgroundHand, foot, and mouth disease (HFMD) is a significant public health challenge, particularly in Guangdong Province, China. Although spatio-temporal dynamics of HFMD have been studied, inter-regional transmission patterns remain underexplored. This study investigates these patterns and the influence of meteorological factors to provide insights for more effective prevention strategies in the context of environmental factors.

methodsWe collected HFMD cases surveillance, population, and meteorological data from 21 cities in Guangdong from 2009 to 2017. Endemic-epidemic multivariate time-series model was used to analyze the case data, decomposing the HFMD risk into endemic, autoregressive, and spatio-temporal components, and investigating the influence of temperature and relative humidity on these components.

resultsHFMD in Guangdong shows distinct seasonal peaks in the second and fourth quarters, with regional clusters in Guangzhou, Shenzhen, Foshan, and Dongguan. The endemic-epidemic model indicates that 86.57% (95%CI: 77.17%-97.08%) of infection risk is due to autoregressive transmission, 9.50% (95%CI: 8.46%-10.65%) to spatio-temporal transmission, and 3.94% (95%CI: 3.33%-4.60%) to endemic factors. Shenzhen, Foshan, Meizhou, and Zhanjiang have autoregressive transmission rates of 89%-94.08%, while Yunfu, Jiangmen, Zhaoqing, and Shantou have import rates of 12.27%-21.44%. HFMD risk is positively correlated with temperatures below 25 °C for the endemic component and above 25 °C for the autoregressive and spatio-temporal components.

conclusionHFMD transmission in Guangdong is primarily driven by the autoregressive component, with temperature influencing different transmission components. Health authorities should enhance local preventive measures during high-risk seasons and adjust control strategies based on various transmission components to mitigate HFMD spread.

Indexed as

Hand, Foot and Mouth DiseaseMeteorological ConceptsChildChild, PreschoolChinaFemaleHumansInfantMaleSeasonsSpatio-Temporal AnalysisTemperatureHand, foot, and mouth diseaseMeteorological factorsSpatio-temporal analysisTransmission patterns

Identifiers

PMID41094355
PMCPMC12522236

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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.