Spatiotemporal Dynamics of Scrub Typhus in Jiangxi Province, China, from 2006 to 2018

Abstract

Background: Scrub typhus (ST) has become a significant potential threat to public health in Jiangxi. Further investigation is essential for the control and management of the spatiotemporal patterns of the disease. Methods: Time-series analyses, spatial distribution analyses, spatial autocorrelation analysis, and space-time scan statistics were performed to detect spatiotemporal dynamics distribution of the incidence of ST. Results: From 2006 to 2018, a total of 5508 ST cases occurred in Jiangxi, covering 79 counties. The number of ST cases increased continuously from 2006 to 2018, and there was obvious seasonality during the variation process in each year, with a primary peak in autumn (September to October) and a smaller peak in summer (June to August). From 2007 to 2018, the spatial distribution of the ST epidemic was significant heterogeneity, and Nanfeng, Huichang, Xunwu, Anyuan, Longnan, and Xinfeng were hotspots. Seven spatiotemporal clusters were observed using Kulldorff’s space-time scan statistic, and the most likely cluster only included one county, Nanfeng county. The high-risk areas of the disease were in the mountainous, hilly region of Wuyi and the southern mountainous region of Jiangxi. Conclusions: Targeted interventions should be executed in high-risk regions for the precise prevention and control of ST.

Spatiotemporal heterogeneity and the long-term impact of meteorological, environmental and socio-economic factors of scrub typhus in China from 2012 to 2018

“…Meanwhile, scattered children and students have always been in the top 4 on the occupational list of scrub typhus, which means that children are a neglected group, and we need to strengthen their education and pay attention to preventive measures(Yao et al, 2019). The risk of scrub typhus also presented obvious temporal heterogeneity.The high-risk seasons were summer (June to August) and autumn (September to November), especially autumn, which was consistent with the results observed in some previous studies(Ren et al, 2019; Yang et al, 2021). It may be related to the dominant vector and animal host of scrub typhus.…”

Epidemiological characteristics and spatiotemporal patterns of scrub typhus in Fujian province during 2012–2020

“…Monthly scrub typhus cases at the county-level in Fujian provide were aggregated for time series analysis. A seasonal-trend decomposition of time series was applied to explore the various characteristics of periodicity and seasonality in incidence over a 9-year period by R software (Version 4.1, Lucent Technologies Bell Laboratories, Auckland, New Zealand) [ 19 , 20 ]. Seasonal and Trend decomposition using Loess (STL, Loess stands for locally estimated scatterplot smoothing) is a versatile and robust filtering method to decompose time series into trend component, seasonal component and random component for the reason that occasional anomalous observations do not affect the estimation of trend and seasonal components.…”

“…The significance level of clusters was determined by a Z score generated by comparison of the Local Moran’s I statistic for the average incidence in each county. A high positive Z score represented that the surroundings had spatial clusters (High-High: high-value spatial clusters or Low-Low: low-value spatial clusters) and a low negative Z score represented the presence of spatial outliers (High-Low: high values surrounded with low values or Low-High: Low values surrounded with high values) [ 19 , 22 ]. Both Global Moran’s I and LISA were performed by ArcGIS software.…”

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

“…In certain patients infection may lead to multiple organ failure or even death [ 6 ]. Over the past decades, scrub typhus has become a significant public health problem in China due to the expansion of areas endemic for the disease and increases in its incidence and infection risk [ 7 ].…”