Environmental Conditions and Mite Vectors Shape the Spatiotemporal Patterns of Scrub Typhus in Guangdong Province, Mainland China

Scrub typhus has emerged as a life-threatening and increasingly prevalent vector-borne disease. While the spatial and temporal distributions of scrub typhus have been studied by the research community, the main driving factors that influence the spatiotemporal patterns of the disease remain under investigation. Using Guangdong Province as a case study, we combined monthly scrub typhus case data from 2010 to 2019 and environmental and vector-related datasets with a Bayesian hierarchical mixed model to elucidate the spatiotemporal characteristics of the disease. This study revealed that the most highly endemic areas of scrub typhus are concentrated in the western and southern parts of Guangdong Province. A distinct bimodal pattern of scrub typhus was observed, with peaks typically occurring from May to July and October to November. The fitted model indicated that forest, cropland, and chigger mites were positively associated with scrub typhus transmission. Furthermore, climate and vectors were identified as key factors shaping the bimodal seasonal patterns of scrub typhus. Despite data-related limitations, including the treatment of population as a time-invariant variable and the binary simplification of vector suitability, sensitivity analyses confirmed the robustness of the model, offering valuable insights for scrub typhus prevention in Guangdong.

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Publication Details

Journal
Tropical Medicine and Infectious Disease
Published
2025-11-20
DOI
https://doi.org/10.3390/tropicalmed10110326
Primary Topic
Vector-borne infectious diseases
Type
article
Field-Weighted Citation Impact
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article

Environmental Conditions and Mite Vectors Shape the Spatiotemporal Patterns of Scrub Typhus in Guangdong Province, Mainland China

Kai Sun, Tingting Kang, Canjun Zheng, Tian Ma et al.
Tropical Medicine and Infectious Disease
Vector-borne infectious diseases
article

Environmental Conditions and Mite Vectors Shape the Spatiotemporal Patterns of Scrub Typhus in Guangdong Province, Mainland China

Kai Sun, Tingting Kang, Canjun Zheng, Tian Ma, Dong Jiang, Peiwei Fan, Mengmeng Hao, Yongqing Bai, Shuai Chen, Qian Wang, Genan Wu, Jiping Dong, Wenqi Xie, F. Ding, Jun Zhuo, Zhong-Hua Meng, Jiaqi Li
article en

Abstract

Scrub typhus has emerged as a life-threatening and increasingly prevalent vector-borne disease. While the spatial and temporal distributions of scrub typhus have been studied by the research community, the main driving factors that influence the spatiotemporal patterns of the disease remain under investigation. Using Guangdong Province as a case study, we combined monthly scrub typhus case data from 2010 to 2019 and environmental and vector-related datasets with a Bayesian hierarchical mixed model to elucidate the spatiotemporal characteristics of the disease. This study revealed that the most highly endemic areas of scrub typhus are concentrated in the western and southern parts of Guangdong Province. A distinct bimodal pattern of scrub typhus was observed, with peaks typically occurring from May to July and October to November. The fitted model indicated that forest, cropland, and chigger mites were positively associated with scrub typhus transmission. Furthermore, climate and vectors were identified as key factors shaping the bimodal seasonal patterns of scrub typhus. Despite data-related limitations, including the treatment of population as a time-invariant variable and the binary simplification of vector suitability, sensitivity analyses confirmed the robustness of the model, offering valuable insights for scrub typhus prevention in Guangdong.

Tropical Medicine and Infectious DiseaseVol. 10(11)
Chinese Center For Disease Control and Prevention (CN), China Academy of Space Technology (CN), Chinese Academy of Sciences (CN), Peking University (CN), Mahidol University (TH), Angkor Hospital for Children (KH), University of Kinshasa (CD), Mahidol Oxford Tropical Medicine Research Unit (TH), Aerospace Information Research Institute (CN), Institute of Geographic Sciences and Natural Resources Research (CN), University of Chinese Academy of Sciences (CN), State Key Laboratory of Remote Sensing Science (CN), University at Buffalo, State University of New York (US)
Openalex Percentile: Top 63%
Vector-borne infectious diseases
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