Risk mapping of scrub typhus infections in Qingdao city, China

Abstract

Background The emergence and re-emergence of scrub typhus has been reported in the past decade in many global regions. In this study, we aim to identify potential scrub typhus infection risk zones with high spatial resolution in Qingdao city, in which scrub typhus is endemic, to guide local prevention and control strategies. Methodology/Principal findings Scrub typhus cases in Qingdao city during 2006–2018 were retrieved from the Chinese National Infectious Diseases Reporting System. We divided Qingdao city into 1,101 gridded squares and classified them into two categories: areas with and without recorded scrub typhus cases. A boosted regression tree model was used to explore environmental and socioeconomic covariates associated with scrub typhus occurrence and predict the risk of scrub typhus infection across the whole area of Qingdao city. A total of 989 scrub typhus cases were reported in Qingdao from 2006–2018, with most cases located in rural and suburban areas. The predicted risk map generated by the boosted regression tree models indicated that the highest infection risk areas were mainly concentrated in the mid-east and northeast regions of Qingdao, with gross domestic product (20.9%±1.8% standard error) and annual cumulative precipitation (20.3%±1.1%) contributing the most to the variation in the models. By using a threshold environmental suitability value of 0.26, we identified 757 squares (68.7% of the total) with a favourable environment for scrub typhus infection; 66.2% (501/757) of the squares had not yet recorded cases. It is estimated that 6.32 million people (72.5% of the total population) reside in areas with a high risk of scrub typhus infection. Conclusions/Significance Many locations in Qingdao city with no recorded scrub typhus cases were identified as being at risk for scrub typhus occurrence. In these at-risk areas, awareness and capacity for case diagnosis and treatment should be enhanced in the local medical service institutes.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China

“…In general, chiggers and hosts were abundant in secondary vegetation, which grows following anthropogenic or natural disturbance. This type of vegetation can serve as a point from which chiggers can attach to passing hosts [ 28 , 29 ]. One study reported that a positive correlation between scrub typhus and rodent density led to a high incidence of scrub typhus [ 30 ].…”

Climate drives the spatiotemporal dynamics of scrub typhus in China

“…Compared with mechanistic modeling approaches that are highly dependent on the well‐identified and temperature‐based biological processes related to vector‐borne disease spread (Messina et al, 2015 ), which ignore the effects of precipitation and relative humidity, statistical modeling approaches can better fit the empirical relationship observed from the more comprehensive historical data to predict the future risk. There are several studies about predicting the potential risk of scrub typhus across different geographic locations (Xin et al, 2020 ; Yao et al, 2019 ; Zheng et al, 2018 ). However, such predictions derived from yearly mean average values of covariates that only represented a long‐term average distribution of the disease risk and could not infer seasonal patterns of scrub typhus.…”

“…The multi-GCM ensemble mean of the predicted number of national scrub typhus cases under different climate change scenarios (RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. Rhombic points show the mean estimates of national scrub typhus cases and error bars are defined as the range.risk of scrub typhus across different geographic locations(Xin et al, 2020;Yao et al, 2019;Zheng et al, 2018). However, such predictions from yearly mean average values of covariates that only represented a long-term average distribution of the disease risk and could not infer seasonal patterns of scrub typhus.By contrast, our analysis goes beyond previous studies regarding the projection of the scrub typhus spread risk by revealing the future spatiotemporal dynamics of the disease.…”

Co-effects of global climatic dynamics and local climatic factors on scrub typhus in mainland China based on a nine-year time-frequency analysis

“…Besides, the connection between rural and urban areas becomes much closer with the development of the society and economics, making the ST variation more complex, i.e., such as the frequency of trade and travel for human beings and the modern agriculture practices [ 31 ]. There is another study confirmed that the gross domestic product, representing the level of urbanization, contributed most for the boost regression tree model in Qingdao City, China [ 42 ]. In other words, the human factors have been becoming another significant impact factor on ST infections, which makes the characteristics not that stable with high wavelet power values in Fig.…”