Bayesian modeling of meteorological drivers of spatiotemporal dengue transmission in Tainan

Liu Lili; Kang Xiaomin; Li Yazhi; Chen Jing

doi:10.11714/acta.snus.ZR20260035

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Bayesian modeling of meteorological drivers of spatiotemporal dengue transmission in Tainan

更新时间：2026-06-30

- Bayesian modeling of meteorological drivers of spatiotemporal dengue transmission in Tainan
- Acta Scientiarum Naturalium Universitatis Sunyatseni Pages: 1-10(2026)
- 作者机构：
  
  1.山西大学复杂系统数学技术山西省重点实验室，山西太原030006
  2.山西大学复杂系统研究所，山西太原030006
  3.山西大学数学与统计学院，山西太原030006
  4.黔南民族师范学院数学与统计学院，贵州都匀558000
  5.山西省疾病预防控制中心，山西太原030012
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20260035
  CLC： R181
- Received：26 January 2026，
  
  Revised：2026-04-23，
  
  Accepted：07 May 2026，
  
  Online First：30 June 2026，
- 稿件说明：
移动端阅览
Liu Lili, Kang Xiaomin, Li Yazhi, et al. Bayesian modeling of meteorological drivers of spatiotemporal dengue transmission in Tainan[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10.
DOI：

Liu Lili, Kang Xiaomin, Li Yazhi, et al. Bayesian modeling of meteorological drivers of spatiotemporal dengue transmission in Tainan[J/OL]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2026, 1-10. DOI： 10.11714/acta.snus.ZR20260035.

摘要

2023年台南市遭受严重的登革热疫情，其传播受到复杂的环境因素影响。分析台南市登革热传播的时空特征，并厘清影响其传播的关键气候因子，可为本地化的疫情监测及科学防控提供参考依据。本研究使用标准差椭圆、莫兰指数、相关性分析、多重共线性诊断以及贝叶斯时空模型，对2023年1—12月台南市的月登革热病例数据和气象记录进行了分析。研究发现，台南市2023年全年病例数时间上呈现“上升-高峰-下降”的变化趋势，空间上表现为“散发-随机-聚集-消退”的模式。各气候因子的相对风险排序依次为降雨日数

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842872&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842859&type=

1.52400005

2.28600001

平均气温

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https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842859&type=

1.52400005

2.28600001

最大瞬间风速

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842872&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842859&type=

1.52400005

2.28600001

降雨量

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842872&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842859&type=

1.52400005

2.28600001

日照时数，不同区域登革热发病风险差异显著，东区、永康区、安平区、北区为高风险区；降雨日数与平均气温是影响登革热传播的关键气候因子。

Abstract

In 2023， Tainan experienced a severe dengue fever epidemic， the spread of which was influenced by complex environmental factors. This study analyzes the spatiotemporal patterns of dengue fever transmission in Tainan and identifies the key meteorological drivers， aiming to provide a scientific basis for local surveillance and evidence-based prevention. Monthly dengue case data and meteorological records for Tainan from January to December 2023 were analyzed using a standard deviational ellipse analysis， Moran’s

， correlation analysis， multicollinearity diagnostics， and a Bayesian spatiotemporal model. The findings reveal a clear temporal pattern of “increase-peak-decline” in case numbers for the full year of 2023. Spatially， the epidemic evolved through four stages：sporadic occurrence， random distribution， clustering， and eventual dissipation. The relative risks of meteorological factors followed the order： number of rainy days

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https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842847&type=

4.74133301

2.79399991

average temperature

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842873&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842860&type=

1.77800000

2.62466669

maximum instantaneous wind speed

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842884&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842908&type=

4.74133301

2.79399991

rainfall

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842884&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=111842908&type=

4.74133301

2.79399991

sunshine duration. The risk of dengue infection varied significantly across districts， with high risk concentrated in the East， Yongkan

g， Anping， and North Districts. The number of rainy days and average temperature were identified as the most critical meteorological drivers. The study provides evidence that targeted interventions focusing on high-risk districts， combined with meteorological early warning systems， can effectively support dengue control.

关键词

Keywords

references

敖琳珺，张昱勤，许欢，等， 2020 . 评估气象对广东省登革热时空预测模型的贡献［J］. 现代预防医学， 47 （ 16 ）： 2899 - 2903 .

陈思秇，孙立梅，马文军，等， 2020 . 基于贝叶斯时空模型广州市手足口病精细时空尺度下发病影响因素分析［J］. 中国公共卫生， 36 （ 8 ）： 1171 - 1176 .

陈业滨，李卫红，黄玉兴，等， 2016 . 广州市登革热时空传播特征及影响因素［J］. 热带地理， 36 （ 5 ）： 767 - 775 .

樊景春，林华亮，吴海霞，等， 2013 . 广东省2006—2011年登革热时空分布特征［J］. 中国媒介生物学及控制杂志， 24 （ 5 ）： 389 - 391 .

葛文鑫，靳珂珂，孙丽娜，等， 2019 . 登革热流行与不同气象因素相关性研究进展［J］. 中国媒介生物学及控制杂志， 30 （ 4 ）： 367 - 370+378 .

郭钜旋，张晓，刘青连，等， 2017 . 广州市海珠区登革热发病与气象因素相关性研究［J］. 热带医学杂志， 17 （ 8 ）： 1090 - 1092+1129 .

李云峰，林良强，张韶华，等， 2021 . 基于GIS的深圳市登革热的时空流行特征和风险影响因素研究［J］. 中华卫生杀虫药械， 27 （ 6 ）： 531 - 535 .

潘花，仇海全，车金星， 2024 . 基于多元线性回归的数学建模成绩预测研究［J］. 南昌工程学院学报， 43 （ 4 ）： 94 - 100 .

武丹，郭志明， 2022 . 一类 Wolbachia 氏菌在蚊群传播数学模型的全局动力学［J］. 中山大学学报（自然科学版中英文）， 61 （ 5 ）： 133 - 143 .

徐维超， 2012 . 相关系数研究综述［J］. 广东工业大学学报， 29 （ 3 ）： 12 - 17 .

周莹，祁钰湘，张萌，等， 2025 . 2015—2023年广东省老年人群登革热本地疫情流行特征及变化趋势［J］. 中华流行病学杂志， 46 （ 12 ）： 2137 - 2144 .

朱钰，郑屹然，尹默， 2020 . 统计学意义下的多重共线性检验方法［J］. 统计与决策， 36 （ 7 ）： 34 - 36 .

Annan E ， Bukhari M H ， Treviño J ， et al ， 2023 . The ecological determinants of severe dengue： A Bayesian inferential model ［J］. Ecol Inform ， 74 ： 101986 .

Gui H ， Gwee S ， Koh J ， et al ， 2022 . Weather factors associated with reduced risk of dengue transmission in an urbanized tropical city ［J］. Int J Environ Res Public Heal ， 19 （ 1 ）： 339 .

Jang M J ， Lee Y ， Lawson A B ， et al ， 2007 . A comparison of the hierarchical likelihood and Bayesian approaches to spatial epidemiological modelling ［J］. Environmetrics ， 18 （ 7 ）： 809 - 821 .

Martínez-Bello D A ， López-Quílez A ， Torres-Prieto A ， 2017 . Bayesian dynamic modeling of time series of dengue disease case counts ［J］. PLoS Negl Trop Dis ， 11 （ 7 ）： e0005696 .

Polrob W ， La-up A ， 2025 . Nonlinear and lagged effects of climate variability on dengue incidence in an urban megacity： A distributed lag non-linear model （DLNM） based study in Bangkok， Thailand ［J］. BMC Public Heal ， 25 （ 1 ）： 4024 .

Waldhör T ， 1996 . The spatial autocorrelation coefficient moran’s I under heteroscedasticity ［J］. Statist Med ， 15 （ 79 ）： 887 - 892 .

World Health Organization ， 2024 . Dengue and severe dengue ［EB/OL］. （ 2024-12 ）［ 2026-01-26 ］. https：//www.who.int/health-topics/dengue-and-severe-dengue#tab=tab_1 https://www.who.int/health-topics/dengue-and-severe-dengue#tab=tab_1 .

Xiang J ， Hansen A ， Liu Q ， et al ， 2017 . Association between dengue fever incidence and meteorological factors in Guangzhou ， China ， 2005- 2014 ［J］. Environ Res， 153： 17 - 26 .

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