TY - JOUR
T1 - Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis
AU - He, Junyu
AU - Wang, Yong
AU - Liu, Ping
AU - Yin, Wenwu
AU - Wei, Xianyu
AU - Sun, Hailong
AU - Xu, Yuanyong
AU - Li, Shanshan
AU - Soares Magalhaes, Ricardo J.
AU - Guo, Yuming
AU - Zhang, Wenyi
N1 - Funding Information:
This work is partly supported by the China Postdoctoral Science Foundation ( 2020M681825 ), the projects from the 13th Five-Year Plan (No. 17SAZ01 and 18QNP063 ) and National Natural Science Foundation of China (No. 42171398 ).
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/12
Y1 - 2022/12
N2 - Background: Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health. Methods: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012–2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. Results: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. Conclusion: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.
AB - Background: Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health. Methods: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012–2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. Results: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. Conclusion: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.
KW - Climate
KW - Clusters
KW - Partial wavelet coherency
KW - Scrub typhus
KW - Wavelet power spectra
UR - http://www.scopus.com/inward/record.url?scp=85139864450&partnerID=8YFLogxK
U2 - 10.1016/j.onehlt.2022.100446
DO - 10.1016/j.onehlt.2022.100446
M3 - Article
C2 - 36277104
AN - SCOPUS:85139864450
SN - 2352-7714
VL - 15
JO - One Health
JF - One Health
M1 - 100446
ER -