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

Abstract

Background: Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.

Methods: A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.

Results: The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.

Conclusions: These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Keywords: Maximum entropy modeling; Negative binomial regression; Risk factors; Scrub typhus.