Mapping ticks and tick-borne pathogens in China

Guo-Ping Zhao, Yi-Xing Wang, Zheng-Wei Fan, Yang Ji, Ming-jin Liu, Wen-Hui Zhang, Xin-Lou Li, Shi-Xia Zhou, Hao Li, Song Liang, Wei Liu (), Yang Yang () and Li-Qun Fang ()
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Guo-Ping Zhao: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Yi-Xing Wang: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Zheng-Wei Fan: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Yang Ji: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Ming-jin Liu: University of Florida
Wen-Hui Zhang: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Xin-Lou Li: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Shi-Xia Zhou: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Hao Li: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Song Liang: University of Florida
Wei Liu: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology
Yang Yang: University of Florida
Li-Qun Fang: State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Understanding ecological niches of major tick species and prevalent tick-borne pathogens is crucial for efficient surveillance and control of tick-borne diseases. Here we provide an up-to-date review on the spatial distributions of ticks and tick-borne pathogens in China. We map at the county level 124 tick species, 103 tick-borne agents, and human cases infected with 29 species (subspecies) of tick-borne pathogens that were reported in China during 1950−2018. Haemaphysalis longicornis is found to harbor the highest variety of tick-borne agents, followed by Ixodes persulcatus, Dermacentor nutalli and Rhipicephalus microplus. Using a machine learning algorithm, we assess ecoclimatic and socioenvironmental drivers for the distributions of 19 predominant vector ticks and two tick-borne pathogens associated with the highest disease burden. The model-predicted suitable habitats for the 19 tick species are 14‒476% larger in size than the geographic areas where these species were detected, indicating severe under-detection. Tick species harboring pathogens of imminent threats to public health should be prioritized for more active field surveillance.

Date: 2021
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DOI: 10.1038/s41467-021-21375-1

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