Analysis of the Spatiotemporal Distribution and Evolutionary Trends of Scrub Typhus in Jiangsu Province from 2006 to 2023

Abstract

Objective

This study aims to analyze the epidemiological characteristics, spatial and temporal distribution patterns, and trends in the evolution of scrub typhus (ST) in Jiangsu Province from 2006 to 2023. Scrub typhus was chosen for this study due to its increasing incidence in Jiangsu Province, its substantial health burden on rural populations, and its relevance as a vector-borne disease influenced by environmental and seasonal factors.

Methods

Data on ST cases in Jiangsu Province from 2006 to 2023 were obtained from the China Disease Control and Prevention Information System. Descriptive statistics were used to summarize the overall epidemiological trends. Spatial autocorrelation analysis (Global and Local Moran’s I) assessed the overall and local distribution patterns of ST cases. while spatial-temporal hotspot analysis identified regions with significant clustering of cases over time, providing insights into potential high-risk areas.

Results

A total of 16,998 ST cases were reported in Jiangsu Province, with an average annual incidence rate of 1.13 per 100,000. The gender distribution showed a male-to-female ratio of 1:1.20. The ages of affected individuals ranged from 3 months to 97 years, with a mean age of 60 years. Farmers represented the largest occupational group, accounting for 84.68% of the cases. The incidence rate showed a significant upward trend (χ²trend = 8484.517, p < 0.001). Peak incidence occurred primarily between October and November. The global Moran’s I index ranged from 0.071 to 0.345. Local autocorrelation analysis revealed that Yancheng and Nantong cities were high-high clustering areas. Spatial-temporal hotspot analysis revealed that hotspots were predominantly located in the northern and central regions of Jiangsu, while the southern region was identified as a cold spot. These hotspots displayed oscillating patterns, with new hotspots emerging in specific areas. Standard deviation ellipse analysis indicated that the epidemic spread continued to expand along the north-south axis, while the east-west axis showed relative stability. Spatial-temporal scanning analysis identified four high-incidence spatial-temporal clustering zones.

Conclusion

The incidence of ST in Jiangsu Province exhibited a significant upward trend, with distinct seasonal peaks between October and November. The epidemic demonstrated a pronounced transmission along the north-south axis, spatial-temporal clustering, and a shifting center of gravity. It is recommended to strengthen surveillance in high-risk areas and implement targeted prevention and control measures during high-risk seasons, particularly for vulnerable populations, to effectively curb the spread of the epidemic.

Introduction

Scrub typhus (ST) is a serious, acute vector-borne infectious disease caused by Orientia tsutsugamushi and transmitted by the larvae of chigger mites [1]. Transmission occurs primarily through the bite of chigger larvae, with rodents serving as important reservoir hosts and vectors [2]. Patients often have a history of outdoor exposure, and the disease typically incubates for 10 to 14 days. Its main clinical symptoms include fever, crusted eschars, and swollen lymph nodes [3]. Severe cases may result in multi-organ dysfunction or even life-threatening complications [4, 5]. Diagnosis of ST is often delayed due to its non-specific symptoms, and the mortality rate is closely linked to treatment timing. The median mortality rate for untreated patients is approximately 6% [6], which decreases to 1.4% with prompt treatment [7].

Unfortunately, there is currently no effective vaccine against scrub typhus [8], leaving antibiotics as the primary treatment option [9, 10].

ST is a naturally occurring zoonotic disease that predominantly affects populations in tropical and subtropical regions across the Asia-Pacific [11]. According to estimates by the World Health Organisation, approximately 1 million individuals are infected annually worldwide [12, 13], with China identified as a significant endemic area [14]. Historical surveillance data indicate that the distribution of ST in China is regionally significant [15], with most cases occurring in the southern and eastern regions [16–18]. Research on scrub typhus in other regions of China has revealed similar spatial-temporal dynamics, such as those observed in southern provinces like Fujian and Anhui, where outbreaks are also seasonal and influenced by agricultural practices. Internationally, studies in Southeast Asia have demonstrated analogous patterns of disease transmission, often correlating with monsoon seasons and agricultural labor activities.

Jiangsu Province, situated in the East Asian monsoon climate zone, transitions between subtropical and warm-temperate zones, providing ideal climatic and ecological conditions for the proliferation of ST [19, 20]. In recent years, the prevalence of ST in Jiangsu Province has increased significantly, with the epidemic’s scope expanding across the region [21]. Surveillance data indicate that the annual incidence rate in Jiangsu Province rose from 0.21 per 100,000 in 2008 to 1.35 per 100,000 in 2017, with all 13 prefecture-level cities in the province affected [22]. Despite this increase, comprehensive studies on the long-term epidemiological characteristics, risk factors, and transmission dynamics of ST in Jiangsu Province remain scarce [23–25]. The application of spatial-temporal methods such as Moran’s I index and hotspot analysis has become a powerful tool in epidemiology. These methods allow for the identification of disease hotspots, temporal trends, and spatial patterns, which can inform targeted prevention strategies. Previous studies have shown that these methods are invaluable for understanding the spread of vector-borne diseases like scrub typhus, especially in regions with complex geographical and seasonal dynamics.

To address this gap, the present study leverages surveillance data from 2006 to 2023 to provide a detailed analysis of ST in Jiangsu Province. By examining its epidemiological characteristics, spatial and temporal distribution patterns, and evolutionary trends, this research aims to inform the development of precise prevention strategies and optimize public health responses. The findings are expected to play a pivotal role in reducing the burden of scrub typhus, mitigating its spread, and safeguarding vulnerable populations in high-risk areas.

Materials and Methods

Study Area

Jiangsu Province is situated in eastern China, within the Yangtze River Delta region, spanning geographic coordinates from 116°21’ to 121°56’ E longitude and 30°45’ to 35°08’ N latitude. With Nanjing as its provincial capital, Jiangsu comprises 13 prefecture-level cities and 95 county-level administrative divisions. The province is characterized by a predominantly flat terrain featuring an intricate landscape of vast plains, lakes, rivers, and low hills. As of the end of 2023, the province’s resident population stood at 85.26 million, while its regional GDP reached 12,822.22 billion yuan, making it one of China’s most economically dynamic regions.

Data Source

Data on ST cases in Jiangsu Province from January 1, 2006, to December 31, 2023, were extracted from the China Disease Control and Prevention Information System. Data duplicates were identified and removed through automated checks in the dataset, and outliers were assessed using the Tukey method. The data were reported on a daily basis. All patients referred to the “Technical Guidelines for the Prevention and Control of Scrub Typhus (Trial Version)” issued by the Chinese Center for Disease Control and Prevention in 2009, which outlined the diagnostic techniques for Scrub Typhus. Based on the epidemiological history, clinical manifestations, and laboratory test results, the cases were diagnosed, and only those with clinical diagnosis and laboratory diagnosis were included. The target population for these data includes individuals of all ages. Population demographic data were obtained from the Jiangsu Statistical Yearbook for the same period. The disease incidence was calculated as the total number of reported cases divided by the total population at risk, multiplied by 100,000, providing the incidence rate per 100,000 population.

Research Methodology

Descriptive Analysis

Descriptive epidemiological methods were used to analyze the epidemiological characteristics of ST in Jiangsu Province from 2006 to 2023. Chi-square and trend tests were conducted after verifying that the data met assumptions of independence and normality. Data were checked for normality using the Shapiro-Wilk test, and independence was confirmed by ensuring no repeated measurements for the same individual. Statistical analyses, including χ² and trend tests, were conducted using SPSS 18.0 software with a significance level of α = 0.05. Spatial data visualization was performed using ArcGIS 10.8 software.

Spatial Autocorrelation Analysis

To evaluate the spatial distribution of ST cases, global spatial autocorrelation (Global Moran’s I) was employed to measure overall distribution patterns, with values ranging from − 1 to 1. A value close to + 1 indicates a high degree of positive spatial autocorrelation, where similar values cluster together; a value near − 1 indicates negative spatial autocorrelation, where dissimilar values are clustered; and a value close to 0 suggests random spatial distribution. In this study, Moran’s I was used to identify regions with significant clustering of ST cases, providing insights into areas at higher risk for disease transmission. while local spatial autocorrelation (Local Moran’s I) was used to identify specific clustering patterns [26]. Clusters were categorized as high-high (H-H), low-low (L-L), high-low (H-L), or low-high (L-H). Statistical significance was assessed using Z-tests (P < 0.05) [27].

Hotspot Analysis (Getis-Ord Gi*)

The local Getis-Ord Gi* hotspot detection is widely used to analyze local spatial autocorrelation. This approach efficiently uncovers localized spatial clustering characteristics and identifies statistically significant clusters of high and low values (hotspots) and clusters (coldspots). By calculating Z-values using the Getis-Ord Gi* formula, specific spatial patterns can be assessed: a positive and statistically significant Z(Gi) indicates that the incidence rate in the surrounding area of region i is relatively high, classifying region i as a hotspot; conversely, a negative and statistically significant Z(Gi) indicates that the incidence rate in the surrounding area of region i is relatively low, categorizing region i as a coldspot.

Analysis of Emerging Spatial-Temporal Hotspots

The emerging spatial-temporal hotspots and coldspots of ST in Jiangsu Province were analyzed using the Getis-Ord Gi* statistical method within the spatial-temporal cube, combined with the Mann-Kendall trend test. The Mann-Kendall trend test evaluates trends through z-scores and P-values: z-scores greater than 1.65 indicate a significant upward trend, z-scores less than − 1.65 indicate a significant downward trend, and z-scores close to 0 indicate no significant change. Based on the P-value, hotspot patterns were further classified into 16 types, including new, persistent, enhanced, and dispersed, to elucidate the developmental dynamics of hotspot areas [28].

Standard Deviational Ellipse

The Standard Deviational Ellipse (SDE) method was applied to analyze the spatial distribution characteristics of the disease quantitatively. The spatial distribution of morbidity and the primary directional trend were examined by calculating the centre of the ellipse, the long and short axes, the directional angle, and the oblateness. The ellipse’s centre represents the centre of gravity of the incidence distribution, reflecting the relative position of ST cases in a two-dimensional space. The directional angle is the angle between the long axis and the north direction, indicating the orientation of the distribution. The ratio of the lengths of the long and short axes (i.e., the oblateness) reveals the degree of spatial concentration or dispersion.

Spatial-Temporal Aggregation Analysis

High-risk aggregation areas were identified using SaTScan v10.1.2 software based on Kulldorff’s spatial-temporal scanning method [29]. The discrete Poisson model analyzed spatial-temporal case clustering across 95 districts and counties. Monthly time intervals, a maximum spatial radius of 50% of the population, and a temporal radius of 20% of the study duration were used. Statistical significance was determined using log-likelihood ratio (LLR) values from 999 Monte Carlo simulations (P < 0.05).

Results

Overview of the Epidemic

From 2006 to 2023, a total of 16,998 cases of ST were reported in Jiangsu Province, with an average annual incidence rate of 1.13 cases per 100,000 population. The highest number of reported cases and the peak incidence rate occurred in 2019, while the lowest was recorded in 2009. Over the study period, the annual reported incidence rate demonstrated a significant upward trend (χ²trend = 8484.517, p < 0.001), as shown in Fig. 1. The temporal distribution of ST incidence revealed a distinct seasonal pattern, with cases predominantly concentrated between September and December. The peak period occurred from October to November, during which 16,148 cases were reported, accounting for 95.00% (16,148/16,998) of the total cases (Fig. 2).

Fig. 1.

Overall trend in the number of cases and incidence rate of ST in Jiangsu province (2006–2023)

Fig. 2.

Monthly distribution of ST incidence (A) and case numbers (B) in Jiangsu province (2006–2023)

Regional Distribution

Between 2006 and 2023, ST cases were reported in all prefecture-level cities across Jiangsu Province. The top three cities with the highest number of reported cases were Yancheng (6,297 cases, 37.05%), Taizhou (2,468 cases, 14.52%), and Suqian (2,326 cases, 13.68%), collectively accounting for 65.25% of the total cases. The number of cities affected by the epidemic increased from 2 in 2006 to 13 in 2023(Figs. 3 and 4A and B).

Fig. 3.

Temporal distribution of case numbers and incidence of ST across cities and counties in Jiangsu province (2006–2023)

Fig. 4.

A. Regional distribution of reported cases of ST in Jiangsu province (2006–2023) B. Annual reported cases of Scrub Typhus in Jiangsu province (2006–2023)

Population Distribution

From 2006 to 2023, a total of 16,998 ST cases were reported in Jiangsu Province, with a male-to-female ratio of 1:1.20. The ages of affected individuals ranged from 3 months to 97 years, with a mean age of 60 years. Notably, 87.01% (14,790 cases) were concentrated in the 41–80 age group, with the highest proportion (31.33%) in the 61–70 age subgroup (Fig. 5A and B). Farmers represented the largest occupational group, accounting for 84.68% (14,394 cases) of all cases. Additionally, a significant upward trend in incidence was observed among the domestic and inactive population during the study period (χ²trend = 41.491, p < 0.001) (Fig. 6).

Fig. 5.

A Gender and age distribution of ST cases in Jiangsu province (2006–2023). B Gender and age distribution of ST cases in Jiangsu province (2006–2023)

Fig. 6.

Occupational distribution of Reported ST cases in Jiangsu province (2006–2023)

Spatial Aggregation of ST in Jiangsu Province

Global and local spatial autocorrelation analyses were performed to evaluate ST incidence rates across Jiangsu Province from 2006 to 2023 at the county and district levels. The global analysis demonstrated statistically significant spatial clustering since 2016, with Z-values consistently exceeding 1.96 (P < 0.05) (Table 1). Local analysis identified “high-high” clusters predominantly in Yancheng and Nantong, while “low-low” clusters were concentrated in Suzhou, Wuxi, Changzhou, and Nanjing (Fig. 7).

Table 1.

Global moran’s index for the incidence of ST in Jiangsu Province (2006–2023)

Year	Moran’ I	Z-Score	p-Value	Cluster
2006	-0.003	0.257	0.797	No
2007	0.077	1.644	0.100	No
2008	0.059	1.366	0.172	No
2009	0.030	1.127	0.260	No
2010	0.071	2.147	0.032	Yes
2011	0.120	2.227	0.026	Yes
2012	0.072	1.437	0.151	No
2013	-0.003	0.210	0.833	No
2014	0.081	1.572	0.116	No
2015	0.103	1.946	0.052	No
2016	0.176	3.039	0.002	Yes
2017	0.114	2.202	0.028	Yes
2018	0.160	2.807	0.005	Yes
2019	0.183	3.603	0.000	Yes
2020	0.254	4.449	0.000	Yes
2021	0.281	4.801	0.000	Yes
2022	0.270	4.551	0.000	Yes
2023	0.345	5.902	0.000	Yes

Fig. 7.

LISA cluster map of the annual incidence of ST in Jiangsu province (2006–2023)

Hotspot Analysis (Getis-Ord Gi*)

An analysis of the spatial distribution and hotspot evolution of ST in Jiangsu Province from 2006 to 2023 revealed a dynamic pattern of hotspot areas, with no coldspot regions identified throughout the study period. The spatial distribution of hotspots demonstrated significant temporal and spatial evolution. Between 2006 and 2010, hotspots gradually expanded from Yancheng to include Huai’an and Yangzhou. From 2011 to 2015, the focus of hotspots further shifted to encompass Taizhou, Nantong, and Yancheng. By 2016–2023, hotspot areas stabilized, concentrating primarily in five key municipal districts: Yancheng, Huai’an, Suqian, Taizhou, and Nantong (Fig. 8).

Fig. 8.

Hotspot distribution of the annual incidence of ST in Jiangsu province (2006–2023)

Analysis of the Temporal and Spatial Hotspots of ST in Jiangsu Province

In this study, spatial-temporal cubes were constructed with a distance interval of 10 km and a time step of 1 year, using the spatial location of ST incidence as x and y coordinates and the time of disease onset as the z coordinate. Between 2006 and 2023, data from 16,998 cases were aggregated into 3,190 10 × 10 km grid cells, covering an area of 580 km from west to east and 550 km from south to north, with a period of 18 years. Of the 3,190 grid cells, 1,070 (33.54%) contained case data in at least one-time step, generating a total of 19,260 spatial-temporal bars. Based on a neighbourhood distance of 30 km, spatial-temporal hotspot analysis identified 816 grid cells exhibiting hotspot or coldspot trends. The Mann-Kendall trend analysis yielded a z-score of 3.1060 with a p-value of 0.0019. During the study period, 378 hotspots and 438 coldspots were detected. Hotspots were primarily concentrated in the northern and central regions of Jiangsu Province, whereas coldspots were distributed in the southern region, separated by areas without significant hotspot or coldspot patterns. Oscillating hotspots dominated the types, with 284 detected, primarily clustered in Yancheng, Suqian, Nantong, Taizhou, and the junction areas of Zhenjiang, Changzhou, and Wuxi. 28 new hotspots were detected around oscillating hotspots, reflecting the gradual outward expansion of hotspot areas. Notably, four strengthening hotspots were observed in the Binhai County region of Yancheng. Among the coldspot types, consecutive coldspots were the most numerous, totalling 180. They were primarily located in Nanjing, Wuxi, Xuzhou, and Lianyungang, as well as parts of southern Huai’an, southern Changzhou, central Suzhou, and southern Nantong. These regions maintained low incidence rates over an extended period. Additionally, 139 intensified coldspots were identified, mainly in northern Nanjing, western Zhenjiang, eastern Xuzhou, and northern Lianyungang, as well as in northern Huai’an, southern Yangzhou, and parts of southern Nantong(Fig. 9).

Fig. 9.

Emerging spatiotemporal hotspot analysis of the incidence of ST in Jiangsu province (2006–2023)

Trends in the Spatial and Temporal Evolution of ST in Jiangsu Province

Between 2006 and 2023, the azimuth angle of ST incidence in Jiangsu Province fluctuated between 4° and 171°. Although the azimuth angle increased from 4.34° to 95.48° in 2009, the short and long axes of the standard deviation ellipse were nearly equal, and the oblateness was close to 1. From 2010 onward, the azimuth angle rose steadily from 25.75° to over 100°.

The short semi-axis length fluctuated between 93 and 195 km, from 93.33 km in 2006 to 181.33 in 2023. The long semi-axis length expanded from 160 km in 2008 to 368 in 2017. Combined trends of the short and long axes revealed a particularly significant expansion in the north-south direction, while changes in the east-west direction remained relatively minor. Specifically, the north-south direction contracted between 2006 and 2009 but continued to expand from 2009 to 2023, forming an elliptical distribution pattern along the northwest-southeast axis (Fig. 10).

Fig. 10.

Standard deviation ellipse analysis of ST incidence in Jiangsu province (2006–2023)

The epidemic centre of gravity primarily remained at the Yancheng, Yangzhou, and Taizhou junctions. The trajectory demonstrated a slow southwest movement from northern Yancheng, followed by a shift to northern Yangzhou, southeastward expansion, and a final return northwest to Yancheng. (Table 2; Figs. 10 and 11).

Table 2.

Shape parameters of the ellipse for the standard deviation of ST incidence in Jiangsu Province (2006–2023)

Year	Azimuth (°)	Short Axis (km)	Long Axis (km)	Aspect Ratio (Major/Minor)
2006	33.31	93.33	160.00	1.71
2007	22.11	137.33	218.67	1.59
2008	4.34	174.67	305.33	1.75
2009	95.48	194.67	196.00	1.01
2010	25.75	144.00	208.00	1.44
2011	145.61	174.67	226.67	1.30
2012	170.43	149.33	281.33	1.88
2013	163.60	136.00	325.33	2.39
2014	150.27	162.67	318.67	1.96
2015	141.13	168.00	312.00	1.86
2016	137.32	170.67	333.33	1.95
2017	137.80	177.33	346.67	1.95
2018	144.00	182.67	326.67	1.79
2019	135.44	180.00	356.00	1.98
2020	137.99	169.33	358.67	2.12
2021	132.87	189.33	360.00	1.90
2022	131.81	180.00	372.00	2.07
2023	130.57	181.33	368.00	2.03

Fig. 11.

Analysis of the spatial evolution of ST in Jiangsu province (2006–2023)

Spatial-Temporal Aggregation Analysis

The spatial-temporal aggregation of ST was primarily concentrated in the northern and central regions of Jiangsu Province over the past 18 years. Four significant aggregation areas were identified through the analysis, including one Type I aggregation area and three Type II aggregation areas, all of which were statistically significant (p < 0.001). Two Type II clusters with a 0 km radius were detected, representing single-county clusters without spatial extension. These were retained in the results for completeness but should be interpreted cautiously.

A significant clustering area was centred in Sheyang County, with a radius of 198.31 km, encompassing 45 counties and districts, including Binhai County, Jianhu County, and Dongtai City. The clustering period was identified as 2014–2021 (RR = 8.02, LLR = 8698.04, p < 0.001). (Table 3; Fig. 12).

Table 3.

Summary of the results from Spatiotemporal scan analysis of chiggers in Jiangsu Province (2006–2023)

Aggregate Type	Convergence Centre (Lat, Long)	Radius of Influence (km)	Counties Covered	Aggregation Time	Actual Number of Incidents	Expected Number of Cases	LLR	RR	p-Value
Type I	33.77 N,120.33E	198.31	45	2014/1/1-2021/12/31	11,394	3439	8698.04	8.02	< 0.001
Type II	33.34 N,120.16E	99.97	6	2011/1/1-2012/12/31	507	123	337.25	4.20	< 0.001
	33.99 N,119.83E	0	1	2006/10/1-2007/12/31	175	17	249.79	10.33	< 0.001
	33.03 N,119.03E	0	1	2006/10/1-2012/12/3	184	28	192.37	6.68	< 0.001

Note: “0 km radius” indicates that the detected cluster includes only one administrative unit, and thus the spatial extent confined to a single location

Fig. 12.

Temporal and spatial scan cluster map of Chiggers in Jiangsu province (2006–2023)

Discussion

Based on ST monitoring data from Jiangsu Province (2006–2023), this study systematically analyzed the epidemic dynamics from temporal, spatial, and spatial-temporal perspectives. It identified the epidemiological patterns and evolution trends of ST and provided a robust theoretical basis for designing effective prevention and control strategies. As a natural focal disease, the spread of ST is influenced by complex interactions among hosts, vectors, pathogens, the natural environment [30], socio-economic factors [31], and human activities [32]. Previous studies confirm that the incidence of ST is closely associated with the distribution of host vectors, climatic conditions [33], and geographical attributes [34], highlighting its significant spatial characteristics [35]. However, traditional epidemiological methods are limited in exploring these spatial properties, making it challenging to uncover the epidemic’s characteristics fully. The application of spatial autocorrelation and spatial-temporal aggregation scanning in recent years has effectively addressed these limitations, providing new insights for early warning systems and optimizing prevention strategies.

The study revealed a significant upward trend in ST incidence over the past 18 years, peaking in 2019. Temporal distribution analyses identified a distinct seasonal and cyclical pattern, with peak incidence occurring in October and November. This aligns with the “autumn-type” epidemic patterns observed in northern provinces such as Anhui [16] and Shandong [36]. However, it differs from the epidemic season in some southern provinces of our country [30, 37]. The occupational distribution showed that farmers constituted the majority of cases (84.68%), likely due to increased exposure to chigger hosts during the autumn agricultural season [38]. Females exhibited a higher incidence rate than males [39, 40], with cases primarily concentrated in individuals aged 41–80, particularly those aged 61–70. This may be attributed to the outmigration of younger males from rural areas to urban centres for work, leaving middle-aged and older women as the primary agricultural labour force. These findings are consistent with similar studies conducted in Fujian and Anhui provinces [16, 41].

Spatial analyses demonstrated an intensifying spatial aggregation of ST, as indicated by the yearly increase in the global Moran’s I index. Local spatial autocorrelation analysis identified “high-high” clusters primarily in Yancheng and Nantong, high-risk regions for epidemic transmission. Meanwhile, “low-low” clusters were concentrated in southern areas such as Nanjing and Wuxi. The formation of “high-high” clusters in Yancheng and Nantong can be attributed to a combination of ecological and socio-economic factors. These areas have extensive agricultural activities, a high proportion of rural residents engaged in outdoor work, and ecological conditions favorable to chigger mites, such as humid environments and dense vegetation. Conversely, the “low-low” clusters in Nanjing and Wuxi are likely due to higher urbanization levels, better living conditions, lower exposure to chigger habitats, and greater access to healthcare resources. These socio-economic advantages may contribute to lower case detection or actual incidence rates.These dynamic changes underscore the need for timely adjustments to prevention and control measures targeting expanding high-incidence regions. While our analysis focused on spatial and temporal trends, the dynamics of ST transmission are complex and influenced by a variety of factors, including climate, geography, and socioeconomic conditions. Previous studies have indicated that climatic changes, land use, and ecological factors, such as the distribution of host animals, may contribute to the formation of hotspots. Additionally, socioeconomic factors, such as population density, economic activity, and agricultural practices, play a critical role in the spread of the disease. Incorporating these environmental and socioeconomic variables into future models could offer a more comprehensive understanding of the epidemic’s evolution and assist in more precise control strategies. Spatial-temporal hotspot analysis revealed that hotspots predominantly exhibited oscillatory patterns and were primarily clustered in Yancheng, Suqian, and Taizhou. New hotspots emerged around these oscillatory hotspots, suggesting a gradual outward expansion of high-incidence areas. Intensified hotspots were observed in Binhai County, indicating a growing epidemic intensity in this region. Conversely, coldspots were concentrated in southern regions such as Nanjing and Wuxi, consistently maintaining low incidence rates.

The epidemic’s centre of gravity followed a complex migration trajectory: shifting from northern Yancheng southwestward, southeastward, and northwestward. This pattern may reflect the influence of factors such as population migration, regional economic development, and environmental changes. The elliptical expansion pattern highlighted significant north-south growth, with limited changes in the east-west direction. These findings stress the importance of enhanced regional coordination along the north-south axis for effective epidemic prevention.Temporal and spatial scanning analyses revealed a consistent expansion of high-incidence clusters, particularly since 2014. Since then, high-incidence areas have covered most regions north of the Yangtze River, with Sheyang County emerging as a key high-risk focus. Enhanced surveillance and prevention measures in these areas are essential to curtail further epidemic spread.

Comprehensive analyses reveal significant spatial-temporal dynamics in the spread of ST in Jiangsu Province. The continued expansion of hotspot areas and the emergence of new hotspots should remain the primary focus of current epidemic prevention and control. Based on the seasonal, regional, and demographic characteristics of the epidemic, the following measures are recommended: (1) Strengthen Surveillance and Early Warning: Focus on expanding hotspot areas, particularly in Yancheng, Suqian, and Taizhou, and implement dynamic tracking of emerging hotspots to identify high-risk areas early. (2) Targeted Interventions: Farmers and non-working populations should be prioritized for health education and early diagnosis campaigns. Managing chigger habitats and developing effective vector control strategies are critical to reducing transmission risk. (3) Optimize resource allocation: Utilize space-time analysis tools to enhance decision-making, accurately allocate prevention and control resources, and increase the efficiency of epidemic prevention and control measures.

In this study, we innovatively introduced spatial-temporal hotspot analysis [42], accurately portraying the hotspot and coldspot characteristics of the spatial-temporal distribution of ST in Jiangsu Province.This methodological innovation enabled a precise identification of high-incidence and low-incidence areas, providing valuable insights into the spatial dynamics of the epidemic. Furthermore, by conducting a comprehensive analysis using the standard deviation ellipse and the center of gravity movement trajectory, the study highlighted significant changes in the geospatial patterns of ST from 2006 to 2023. These findings offer a new perspective on the trends of epidemic evolution and emphasize the importance of integrating spatial-temporal tools in epidemiological research.Despite these strengths, several limitations should be acknowledged. First, the study does not fully integrate environmental and socioeconomic factors, which are critical for understanding the multifaceted nature of ST transmission. Second, potential biases in the data sources, such as underreporting or incomplete data, may have influenced the robustness of the results [43]. Future research should employ multifactor models that incorporate climatic, ecological [44], socioeconomic, and human activity-related variables to better elucidate the drivers of the epidemic. Such an approach will contribute to a more comprehensive theoretical foundation for developing scientifically informed and refined prevention strategies.

While this study utilized multiple methods to analyze the spatio-temporal distribution of scrub typhus, it is important to acknowledge the limitations of these approaches. Spatial autocorrelation methods, for example, assume that disease spread follows a constant pattern across the entire study area, which may not account for variations due to localized environmental or socioeconomic factors. Temporal trend analysis, while useful for identifying long-term trends, does not capture real-time fluctuations in disease incidence. Furthermore, spatial-temporal hotspot analysis is dependent on the scale of the study area and the temporal periods chosen, which may impact the detection of emerging outbreaks. Future studies could consider integrating more dynamic models that account for such limitations.

Conclusions

Between 2006 and 2023, the incidence of ST in Jiangsu Province demonstrated a significant upward trend characterized by distinct spatiotemporal dynamics and a gradual expansion of its epidemiological range. The epidemic exhibited a particularly pronounced transmission pattern along the north-south axis, with evident spatiotemporal clustering and a shifting geographical epicenter. Based on these findings, it is recommended to enhance surveillance in identified high-risk areas and implement targeted prevention and control measures, especially during peak transmission seasons and for vulnerable populations, to mitigate the spread of the disease effectively.

Abbreviations

ST

Scrub typhus

SDE

Standard Deviational Ellipse

RR

Relative risk

LLR

Log-likelihood ratio

Author Contributions

P-LZ, C-JB and W-LK designed and conducted the research. X-QC and LX are responsible for data management.X-QC, LX, W-XG and X-FZ analyzed the data and interpreted theresults. X-QC and LX prepared the first manuscript. All authors critically reviewed, interpreted the results and approved the final manuscript, P-LZ, C-JB and W-LK had primary responsibility for the final content.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Ethics Approval and Consent to Participate

This effort of disease control was part of CDC’s routine responsibility in Jiangsu Province, China. Therefore, institutional review and informed consent were not required for this study. All data analyzed were anonymized, and does not contain any personal privacy or identity information.

Consent for Publication

All authors revised the manuscript and gave the consent to submit and publish the paper.

Competing Interests

The authors declare no competing interests.