Skip to main content

Spatio-temporal variations of emerging sites infested with schistosome-transmitting Oncomelania hupensis in Hunan Province, China, 1949–2016



Constant emerging sites infested with Oncomelania hupensis (O. hupensis) impede the goal realization of eliminating schistosomiasis. The study assessed the spatial and temporal distributions of new Oncomelania snail habitats in Hunan Province from 1949 to 2016.


We used the data from annual snail surveys throughout Hunan Province for the period from 1949 to 2016. Global Moran’s I, Anselin local Moran’s I statistics (LISA) and a retrospective space-time permutation model were applied to determine the spatial and temporal distributions of emerging snail-infested sites.


There were newly discovered snail-infested sites almost every year in 1949–2016, except for the years of 1993, 2009 and 2012. The number of emerging sites varied significantly in the five time periods (1949–1954, 1955–1976, 1977–1986, 1986–2003 and 2004–2016) (H = 25.35, p < 0.05). The emerging sites lasted 37.52 years in marshlands, 30.04 years in hills and 24.63 at inner embankments on average, with the values of Global Moran’s I being 0.52, 0.49 and 0.44, respectively. High-value spatial clusters (HH) were mainly concentrated along the Lishui River and in Xiangyin County. There were four marshland clusters, two hill clusters and three inner embankment clusters after 1976.


Lower reaches of the Lishui River and the Dongting Lake estuary were the high-risk regions for new Oncomelania snail habitats with long durations. Snail surveillance should be strengthened at stubborn snail-infested sites at the inner embankments. Grazing prohibition in snail-infested grasslands should be a focus in marshlands. The management of bovines in Xiangyin County is of great importance.

Graphical Abstract


Schistosomiasis japonica is a chronic and debilitating disease that also exacerbates poverty [1]. The World Health Organization (WHO) estimated that approximately 200,000 deaths were caused by schistosomiasis and 290.8 million people required preventive treatment for schistosomiasis worldwide in 2018 [1,2,3]. Schistosomiasis caused a loss of nearly 2 million disability-adjusted life years in 2016 [4].

In China, schistosomiasis is caused by trematode blood fluke Schistosoma japonicum (S. japonicum). As the sole intermediate host, Oncomelania hupensis (O. hupensis) plays a critical role in the transmission of schistosomiasis japonica [5], and the geographic distribution of snails determines the occurrence of schistosomiasis [6]. The schistosomiasis-endemic areas in China are classified into three types according to eco-geographical characteristics: (i) marshland and lake regions, (ii) mountainous and hilly regions and (iii) plain regions with waterway networks [7]. Hunan Province retains the first two types, and the marshland and lake-endemic area is further divided into subtypes (marshland and inner embankment) according to the variability of hydrology [8]. Dongting Lake and its tributaries, lying in the northeast of Hunan Province, dominate the marshland and lake regions outside the embankments. During the spring and summer flooding season, the lake and rivers inundate a large area outside the embankments while in the winter they shrink considerably [9]. The characteristic of ‘marshlands in winter and water in summer’ is ideal for the survival of O. hupensis. The measures of returning farmland have enlarged the marshland as well as the snail area [9].

WHO has set the target of ‘morbidity control’ by 2020, which aims to reduce the prevalence of heavy-intensity infection to ≤ 5% among school-aged children [10], and a recommended strategy is to control the freshwater snails that transmit schistosome parasites [11]. Snail control measures are constantly conducted in Hunan Province, but emerging sites infested with O. hupensis occur frequently [12]. O. hupensis were eliminated in most original places in the 1950s, but they could immigrate from the original sites to places with a more suitable environment [13]. Because of enlarged areas of marshland with widely distributed and complicated habitats of snails, the past experience of large-scale reclamation is no longer suitable for the purpose of eliminating snails [8]. In our study, a snail habitat is defined as the area where the snails can survive and reproduce. It is important to identify and monitor new snail habitats to effectively control schistosomiasis [14]. This study aimed to investigate the spatio-temporal patterns of the new Oncomelania snail habitats in Hunan Province from 1949 to 2016.

Materials and methods

Study sites

The study was conducted in Hunan Province, China (Fig. 1), which is located on the south bank of the Yangtze River, situated between 108°47′–114°16′ east longitude and 24°37′–30°08′ north latitude [15]. As a humid subtropical monsoon climate zone, winter is short, cool and damp, and summer is hot and humid, with an annual rainfall of 1300–1800 mm. The temperature is about 3 to 8 ℃ in January and 27 to 30 °C in July [16]. Hunan has 45.9% of the total snail-infected area (3.85 billion square meters) in the country. Dongting Lake lies in the northeast of Hunan Province, connecting water with the lower branch of the Yangtze River. The characteristic of ‘marshlands in winter and water in summer’ is ideal for the survival of O. hupensis. Four rivers (Xiang, Zi, Yuan and Li) converge into the Yangtze River at Dongting Lake [16]. An inner embankment is defined as an area close to the lake but inside the embankment. Foreign snails can effortlessly migrate from marshlands to inner embankments. Mountainous habitats of Hunan provinces account for 17% of all snail mountainous habitats in The People’s Republic of China [17]. In the mountainous and hilly regions, the snails are mainly distributed in ditches and rice paddies [18].

Fig. 1
figure 1

Geographical location of the study region in Hunan Province of China


The annual snail surveys carried out throughout Hunan Province from 1949 to 2016 contain information on the location, area, and first and last years of emerging snail-infested sites. An emerging snail-infested site was defined as a new site infested with the snails in a particular year. The first year of an emerging site was defined as the earliest year to identify living O. hupensis in the site. The last year of an emerging site was the year that the snails disappeared from the site. The duration of an emerging snail-infected site was the difference between the last year and the first year of the emerging site.

The annual snail surveys are performed using a traditional random equidistant frame survey method combined with random quadrant sampling (0.11 m2-sized frames) directly. The frames are set 20 × 20 m apart when the area outside the embankments is < 200,000 km2 or 50 × 30 m apart when the area is > 200,000 km2. For the inner embankments, the frames are set at 5 m × 10 m apart. All captured snails are examined to identify living ones by microscope after crushability. The latitude and longitude of snail-infested sites were obtained by a handheld global positioning system (GPS). The distribution maps were drawn by Google Earth via key inflexion points.

Quality control

A professional control system for snail field surveys was established after the People's Republic of China (PRC) was established in 1949 [6] and provides valuable historical records about emerging snail habitats. The survey results are published every year [19, 20]. The quality control for each snail survey includes professional training of investigators, supervision by provincial officials and reexamination of snail samples [21].

Descriptive analysis

A database was established using Microsoft Excel 2016 (Microsoft Inc. Washington, DC, USA). Descriptive analysis was performed using the statistical software package R 3.6.1 (AT&T Bell Laboratories, New Zealand). In line with the national schistosomiasis control programs in China [22], we examined the differences in the spatio-temporal variations of emerging snail-infested sites among five time periods: preparation period (1949–1954), mass campaign period (1955–1976), achievement consolidation period (1977–1986), morbidity control period (1986–2003) and comprehensive strategy period (2004–2016). Kruskal-Wallis tests were performed to determine the difference in the average number of snail-infested sites among five time periods. A coordinated Nemenyi pairwise comparison was made for the duration of emerging snail-infected sites among three geographical categories (marshlands, inner embankments and hills).

Spatial autocorrelation analysis

Global Moran’s I was applied to detect spatial autocorrelation for the durations of emerging snail-infected sites among the three categories (marshlands, inner embankments and hills). A row standardization (a technique for adjusting the weights in a spatial weight matrix) was conducted. The method of inverse distance was applied to determine the spatial relationship. The value of the Moran's I ranges between −1 (maximum negative association) and 1 (maximum positive association), with a zero value indicating a random spatial pattern [23]. A Z-score > 1.96 or < \(-1.96\) and a two-sided p value < 0.05 were considered to be statistically significant.

Local indicators of spatial association (LISA) were used in the present study to detect the actual location of significant spatial clusters and outliers [24]. It was adopted to explore significant hotspots (HH: high-value spatial clusters with long durations), coldspots (LL: low-value spatial clusters with short durations) and outliers (HL: high values surrounded with low values, LH: low values surrounded with high values) of durations by calculating local Moran’s I index. A high positive z score indicated that the surroundings had spatial clusters and a low negative z score indicated the presence of spatial outliers [24]. The LISA map was based on 999 permutations, and features with a p-value < 0.05 were considered statistically significant [25]. All spatial processing and mapping were carried out in ArcGIS 10.0 (ESRI, Inc. Redlands, CA, USA).

Spatio-temporal analysis

The data of each site with longitude, latitude and initial year of snail discovery were imported into SaTScan 9.6 (Kulldorff & Information Management Services, Inc., Bethesda, MD, USA) for retrospective spatiotemporal aggregation analysis. The space-time permutation model requires only case data. The number of attribute values in the time-space window complied with the hypergeometric distribution, and the expected incidence μ(z) of each scanning window Z was calculated according to the characteristics of the hypergeometric distribution [26]:

$$\mu \left( Z \right) = \mathop \sum \limits_{{\left( {Z,d} \right) \in Z}} \left[ {\frac{1}{{n_{G} }}\left( {\mathop \sum \limits_{Z} n_{Zd} } \right)\left( {\mathop \sum \limits_{d} n_{Zd} } \right)} \right]$$

The p-value was generated using the Monte Carlo hypothesis testing method with 999 simulations, and the cut-off point was set at 0.05 [27]. Maximum spatial cluster size was limited to 50% of the population at risk, and the minimum temporal cluster size was 1 year.


Time and space distributions of emerging O. hupensis snail-infested sites

Figure 2 shows the number of emerging snail-infested sites in the five periods (Kruskal-Wallis test: H = 25.35, P < 0.05). Compared with the preparation period (1949–1954), the number of emerging sites increased up to 318.24 per year in the second period. Then, the number declined by 78.07% (248.46 per year) in the third period (1977–1986) and 91.02% (289.67 per year) in the fourth period (1987–2003) compared with the second period. However, the number increased 22.51% (6.43 per year) during the fifth period (2004–2016) compared with the fourth period. Geographically, the emerging snail-infested sites in the preparation period (1949–1954) clustered mostly at the junction of East Dongting Lake and South Dongting Lake with a total area of 9.8 × 109 km2. In the second period, the emerging sites showed a diffuse distribution with an infected area of 4.34 times larger (5.3 × 1010 km2) compared with the first period. In the third period, > 600 snail-infested sites were discovered, but the infected area decreased by 68.02% compared with the second period. Although the number of emerging sites was the lowest in the fourth period, the infected area was 30.83% larger compared with the third period. The emerging sites dispersed mainly along Lishui River in the fifth period. Average infested area was 30.32% smaller than that in the fourth period. The space distribution of emerging O. hupensis snail-infested sites in the five periods is shown in Fig. 3.

Fig. 2
figure 2

Number of emerging snail-infested sites in five periods (1949–1954, 1955–1976, 1977–1986, 1986–2003 and 2004–2016), Hunan Province

Fig. 3
figure 3

Distribution of emerging snail-infested sites in Hunan Province in different time periods. The site with the lightest red part was discovered in 1949–1954, and so on. The darkest red part was for 2003–2016

Description and spatial autocorrection of durations

Mean and median durations of emerging snail-infected sites were 29.68 years and 23 years overall and varied across geographical locations. The time span of the sites was 67 years both inside and outside the embankments, but was 70 years in hilly areas. Mean duration of the emerging sites was longer in regions outside embankments than that in hilly areas (37.52 vs. 30.04 years) and was the shortest in regions inside embankments (24.63 years) (Kruskal-Wallis test: H = 471.63, P < 0.05). Global Moran’s I showed a significant positive spatial autocorrelation for the durations of emerging snail-infected sites in all three schistosomiasis-endemic categories in Hunan Province (Moran’s I > 0.4, P < 0.01) (Table 1).

Table 1 Value of Moran’s I and its statistical significance for snail habitats in Hunan Province

Significant clustering (HH, LL, HL and LH) is shown in Fig. 4. In all types of emerging snail-infested aread, there were many more HH clusters than HL and LH outliers. In the sites confirmed positive by the annual snail census, there were more HH clusters than LL clusters, indicating more frequent clustering for snail-infected sites with a longer duration than those with a shorter duration. HL villages had the fewest in the three categories.

Fig. 4
figure 4

Anselin local Moran's I clustering of durations among a inner embankments, b in marshlands and c in hills. The pink parts indicate HH clusters where snails were in aggregation. The red and blue parts indicate HL outliers and LH outliers where snails may spread

In the area of inner embankments, HH clusters were found in the north-western part of Dongting Lake. LL clusters at inner embankments were observed in the west of East Dongting. HH clusters in marshlands were mainly concentrated along Lishui River and Yuanshui River. LH outliers were mainly distributed in the center of Dongting Lake and the corner between East Dongting and South Dongting. In the area of hills, HH clusters were mainly distributed in Xiangyin County.

SaTScan clusters of emerging snail-infested sites

Inner embankments

A total of 4475 emerging snail-infested sites were included in the space-time analysis to describe the spatial distribution of inner embankments and to investigate the presence of high-density clusters. Eleven statistically significant clusters (P < 0.05) were identified by the space-time permutation model (Table 2, Fig. 5).

Table 2 Clusters of emerging snail-infested sites inside embankments from 1949 to 2016
Fig. 5
figure 5

Spatial distribution of clusters of the emerging snail-infested sites of the inner embankments. Snails were spatially concentrated in clusters. The tighter they gathered, the smaller the cluster was

Cluster 6 (1984–2011) with a radius of 12.34 km2, located in the northwest of Dongting Lake, had the longest duration of 28 years, and the observed to expected ratio (O/E ratio) for the number of infected sites was 20.19. Cluster 9 (2006–2015), located in the northwest of cluster 6 with a radius of 7.33 km2, had the second longest duration of 10 years, and the O/E ratio was 46.26, the largest among all clusters.


Among the 1101 emerging snail-infested sites in outside embankments included in the study, there were 12 significant clusters (Table 3, Fig. 6). Only Clusters 4 and 8 existed > 2 years, with durations of 14 and 2 years, respectively. Cluster 4 (2003–2013), located in the northwest of Dongting Lake, had a radius of 20.56 km2. Cluster 5 (2015) was the most recent one, but only existed for 1 year. Cluster 5, located in the northwest of Cluster 4, had a radius of 8.48 km2 and an O/E ratio for the number of infected sites of 33.82.

Table 3 Clusters of emerging snail-infested sites outside embankments from 1949 to 2016
Fig. 6
figure 6

Spatial distribution of clusters of the emerging snail-infested sites in marshlands. They mainly gathered along rivers


There were six significant clusters in hills (Table 4). Cluster 5 (1996–1998) existed in the fourth period and was located in the northwest of Dongting Lake, with an O/E ratio for the number of infected sites of 15.65. Cluster 6 (1970–1984) had a largest radius (34.57 km2) and the longest duration (15 years) but the fewest observed sites among all clusters. It appeared in the third period in the north of Dongting Lake, with an O/E ratio of 8.69. Cluster 1 had the largest number of infected sites and a radius of 27.52 km2. Other clusters only appeared in the second period (Fig. 7).

Table 4 Cluster of emerging snail-infested sites in hills from 1949 to 2016
Fig. 7
figure 7

Spatial distribution of clusters of the emerging snail-infested sites in hills. They mainly gathered around streams, away from marshlands and lake regions

Combination of LISA and SaTScan

The clusters after 1976 were included, and the combination of LISA and SaTScan screened the most significant zones (Fig. 8). In the categories of inner embankments, HH clusters did not overlap with Clusters 6 and 9. Cluster 1 covered the HL outlier. In the region of marshlands, Clusters 4 and 6 covered most of the HH clusters. However, Clusters 5 and 12 covered the zone with no significance in LISA. In the hills, Cluster 5 covered the LL clusters, and Cluster 6 covered the area with no significance in LISA.

Fig. 8
figure 8

Combination of LISA and SaTScan: a inner embankments, b in marshlands and c in hills. Hotpots of LISA and clusters with long durations (> 1 year) were combined in maps, and the overlapped regions were at high risk


Annual snail surveys and monitoring of suspected snail-infested environments has been successively conducted since 1949 in Hunan Province. Although snail control has always been ongoing, new habitats of Oncomelania snails have emerged frequently. Earlier epidemiological surveys during 1950–1953 [15] and later [28] discovered a large number of new snail habitats in Hunan Province. Although an elimination strategy was implemented and resulted in a great decrease of the snail-infected area during 1956–1976 [29], there have been many new habitats of Oncomelania snails since 1977. Most of the clusters in SaTScan existed in the 1950s and 1960s, and the clusters after 1976 reflected the emerging snail-infested sites. These emerging snail habitats mainly came from the diffusion of the original sites. Genetic analysis demonstrated that snails in emerging habitats were introduced through migration from original sites [30]. From 1977 to 2016, emerging snail-infected sites were discovered in most years, with the exception of 1993, 2009 and 2012, indicating that snail control is a tough mission. Despite the prevalence of schistosomiasis had been decreasing, the number of emerging habitats of O. hupensis continued to increase from 2003 to 2016.

The results from the global spatial autocorrelation analysis indicated spatial heterogeneity in the durations of emerging snail habitats for all the categories of regions. The marshlands are noteworthy for its overwhelming proportion (99.64%) [31]. In marshlands, the Lishui River’s upstream had fewer HH clusters than the middle and lower reaches. The fast flows in the upper reaches threaten the survival of snails [32]. The reasons for long durations in the lower reaches could be bend flow and deposited silt, which are suitable for snails’ growth and migration [32]. The anomaly of the multi-year average runoff has increased in the four rivers since the operation of the Three Gorges Dam, leading to vast grass-covered marshlands and areas for snails [33, 34]. A previous study also showed that the dominant effect of vegetation was reinforced in the north of the Lishui River plain [35], so that the vegetable coverage and specific vegetation types should be considered in snail control programs in the region. The practice of planting fast-growing and adapting trees leads to changes in soil temperature, humidity, pH values and vegetation density, which can make the land less susceptible to O. hupensis [36]. Other HH clusters to be concerned are in the highlands of the Xiangjiang River estuary, mainly in Xiangyin County. The bovine population in Xiangyin County ranks first in Hunan Province [31], and free-range of cattle may contributes to the spread of schistosomiasis. The combination of LISA and SaTScan showed that the lower reaches of Lishui River and Dongting Lake estuary were at high risk. Since LISA focuses on geographical connections while SaTScan on both space and time, the results are not entirely consistent.

By comparing the results of Local Moran’s I statistics and SaTScan, we found overlaps between HH areas in LISA and long-duration circles in SaTScan. The two spatial methods provided consistent results in detecting clusters. After the floods in 2014, 1983 and 1995, respectively, Clusters 5 (2015), 6 (1984) and 12 (1996) emerged subsequently [37, 38]. Flooding and unstable water levels as well as vast marshland regions are likely the main factors leading to the long existence of emerging sites in marshlands. The emerging sites were mostly in the basins of the four rivers, which are frequently inundated by flooding. Snails can be adsorbed on floating debris and spread with water flows [39] or directly spread with the flood water when embankments collapse [39, 40]. Flooding can accelerate the siltation and the formation of new marshland, which is conducive to the expansion of emerging snail habitats [39], and increase the emerging snail-infested sites over the next several years [41]. Dongting Lake is still at high risk for severe flooding and water logging in dyke-enclosed places after the full operation of the Three Gorges Dam [33, 42]. It is difficult to eliminate snails in marshlands by molluscicide or environmental modification. Although great efforts have been made toward snail elimination, new snail-infested sites appear frequently, which is a big challenge for snail control program development. Since the water level is difficult to control, grazing prohibition in snail-infested grasslands should be the first priority [36].

The durations of emerging snail habitats at inner embankments were longer than those in marshlands after 1976. Emerging snail-infested sites at inner embankments were stubborn ones, and those in marshlands were relatively transient. The clusters were not likely to be totally independent. The inner embankment area is isolated from the marshland by an embankment, but is connected to the marshland by sluice gates. There has been effective snail control management inside the embankments [43]. Once snails spread into the inner embankments with flood water, it may take several years to remove them completely. A thick riverside settlement and the redirection of available water for irrigation systems may also contribute to a long duration of snail-infected sites [44]. The geographical environment of the endemic areas does not change fundamentally, and the crisscross ditches and climate are suitable for snail survival [45]. The surveillance around old stubborn snail-infested areas and environmental modification should be strengthened at the inner embankments.

There are also hilly snail-infested regions in Hunan. It is difficult to eradicate snails in hills because of their complex environment such as loose soil and stony land [1, 45]. Geographically, most of hilly snail-infested regions are away from the marshland and lake regions and are rarely affected by flooding. The inner embankment area is isolated from the marshland by an embankment, but is connected to the marshland by sluice gates. The inner embankment is rarely affected by flooding, but the marshland is.

A limitation of the study is the lack of historical snail density information, which hinders a further quantitative analysis. Environmental factors need to be further explored for emerging snail-infected sites in different geographical regions. Given the different spatial analysis units, the clusters detected were not exactly in the same areas. For example, the HH clusters of inner embankments did not overlap with Clusters 6 and 9.


The lower reaches of Lishui River and the Dongting Lake estuary are high-risk regions for new Oncomelania snail habitats with long durations. The snail surveillance should be strengthened around the stubborn snail-infested sites at the inner embankments. The grazing prohibition in snail-infested grasslands should be a focus in marshlands. The management of bovines in hills is of great importance, especially in Xiangyin County.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



World Health Organization

S. japonicum:

Schistosoma japonicum


Local Indicators of Spatial Association

O. hupensis:

Oncomelania hupensis


Global Positioning System


People's Republic of China


High-value spatial clusters


Low-value spatial clusters


High values surrounded with low values


Low values surrounded with high values


  1. Wang L, Utzinger J, Zhou X. Schistosomiasis control: experiences and lessons from China. Lancet. 2008;372:1793–5.

    Article  Google Scholar 

  2. Zhu G, Fan J, Peterson AT. Schistosoma japonicum transmission risk maps at present and under climate change in mainland China. PloS Negl Trop Dis. 2017;11:e6021.

    Article  Google Scholar 

  3. WHO. Global health estimates 2016: deaths by cause, age, sex, by country and by region, 2000-2016. Geneva: World Health Organization; 2018. Accessed 20 May 2020.

  4. Hay SI, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390:1260–344.

    Article  Google Scholar 

  5. Yuan H, Jiang Q, Zhao G, He N. Achievements of schistosomiasis control in China. Mem Inst Oswaldo Cruz. 2002;97(Suppl. 1):187–9.

    Article  Google Scholar 

  6. Zhang SQ, Sun CS, Wang M, Lin DD, Zhou XN, Wang TP. Epidemiological features and effectiveness of schistosomiasis control programme in Lake and Marshland Region in the People’s Republic of China. Adv Parasitol. 2016;92:39–71.

    Article  Google Scholar 

  7. Hui F, Xu B, Huang H, Yu Q, Gong P. Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. Int J Remote Sens. 2008;29:5767–84.

    Article  Google Scholar 

  8. Toor J, Alsallaq R, Truscott JE, Turner HC, Werkman M, Gurarie D, et al. Are we on our way to achieving the 2020 goals for schistosomiasis morbidity control using current World Health Organization guidelines? Clin Infect Dis. 2018;66:S245–52.

    Article  Google Scholar 

  9. Wood CL, Sokolow SH, Jones IJ, Chamberlin AJ, Lafferty KD, Kuris AM, et al. Precision mapping of snail habitat provides a powerful indicator of human schistosomiasis transmission. Proc Natl Acad Sci U S A. 2019;116:23182–91.

    Article  CAS  Google Scholar 

  10. Yuan Y, Xu X, Dong H, Jiang M, Zhu H. Transmission control of schistosomiasis japonica: implementation and evaluation of different snail control interventions. Acta Trop. 2005;96:191–7.

    Article  Google Scholar 

  11. Zhang J, Yue M, Hu Y, Bergquist R, Su C, Gao F, et al. Risk prediction of two types of potential snail habitats in Anhui province of China: model-based approaches. PloS Negl Trop Dis. 2020;14:e8178.

    Google Scholar 

  12. Kurniawan M, Supriatna, Dimyati M. Analysis of spatial distribution of habitat Oncomelania hupensis using landsat etm data in Lindu plain, Central Sulawesi. IOP Conf Ser Earth Environ Sci. 2019; 311: 12015.

  13. Zhihong ZJRG. Historical recollection and experience of schistosomiasis control in Hunan province. J Trop Med Parasitol. 2019;04:194–8.

    Google Scholar 

  14. Yuan Y, Zhang C, Zeng G, Liang J, Guo S, Huang L, et al. Quantitative assessment of the contribution of climate variability and human activity to streamflow alteration in Dongting Lake, China. Hydrol Process. 2016;30:1929–39.

    Article  Google Scholar 

  15. Wu J, Zhou Y, Li L, Zheng S, Liang S, Coatsworth A, et al. Identification of optimum scopes of environmental factors for snails using spatial analysis techniques in Dongting Lake region, China. Parasit Vectors. 2014;7:216.

    Article  Google Scholar 

  16. Liu Y, Zhou YB, Li RZ. Epidemiological features and effectiveness of schistosomiasis control programme in mountainous and hilly region of The People’s Republic of China. Adv Parasitol. 2016;92:73–95.

    Article  CAS  Google Scholar 

  17. Liu Y, Zhong B, Wu Z, Liang S, Qiu D, Ma X. Interruption of schistosomiasis transmission in mountainous and hilly regions with an integrated strategy: a longitudinal case study in Sichuan, China. Infect Dis Poverty. 2017;6:79.

    Article  Google Scholar 

  18. Liying W, Qingwu J, Jianxiang L. Endemic status of schistosomiasis in China in 1999. Chin J Schistosomiasis Control. 1999;6:321.

    Google Scholar 

  19. Guangping L, Ling T, Yiyi L. Endemic status of schistosomiasis in Hunan Province in 2014. J Trop Med Parasitol. 2014;3:127–30.

    Google Scholar 

  20. Xia M, Zhou J, Zhao Z. Distribution of Oncomelania hupensis snails in Hunan Province, 2016. Pact Prey Med. 2016;25:1317–21.

    Google Scholar 

  21. Xu J, Steinman P, Maybe D, Zhou XN, Lv S, Li SZ, et al. Evolution of the national schistosomiasis control programmes in the People’s Republic of China. Adv Parasitol. 2016;92:1–38.

    Article  CAS  Google Scholar 

  22. Niu Y, Li R, Qiu J, Xu X, Huang D, Shao Q, et al. Identifying and predicting the geographical distribution patterns of Oncomelania hupensis. Int J Environ Res Public Health. 2019;16:2206.

    Article  Google Scholar 

  23. Anselin L. Local indicators of spatial association—LISA. Geogr Anal. 1995;27:93–115.

    Article  Google Scholar 

  24. Mitchel A. The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics. 2nd ed. ESRI press; 2005.

  25. Feng Han FS. Effect of global Moran’s I and space-time permutation scanning method in Shanghai metro traffic based on ecological transportation system. Ekoloji Dergisi. 2020;107:4741–9.

    Google Scholar 

  26. Chuang T, Ng K, Nguyen LT, Chaves FL. Epidemiological characteristics and space-time analysis of the 2015 dengue outbreak in the metropolitan region of Tainan City Taiwan. Int J Environ Res Public Health. 2018;15:396.

    Article  Google Scholar 

  27. Pengcheng YXD. Study on snail control in Dongting Lake area of Hunan from 1956 to 1965. J Wuling. 2013;38:20–4.

    Google Scholar 

  28. Zhou XN, Wang LY, Chen MG, Wu XH, Jiang QW, Chen XY, et al. The public health significance and control of schistosomiasis in China–then and now. Acta Trop. 2005;96:97–105.

    Article  Google Scholar 

  29. Qiu C, Lu D, Deng Y, Zou H, Liang Y, Webster JP. Population genetics of Oncomelania hupensis snails, intermediate hosts of Schistosoma japonicum, from emerging, re-emerging or established habitats within China. Acta Trop. 2019;197:105048.

    Article  Google Scholar 

  30. Hui-lan W, Jie Z, Ben-jiao H, Qiong J, Ling T, Meng X, et al. Analysis on the endemic status of schistosomiasis in Hunan province in 2018. J Trop Med Parasitol. 2020;18:37–40.

    Google Scholar 

  31. Qun C. The historical changes of the West Dongting Lake area and the Yuan and Li rivers and the prevalence of schistosomiasis in Hanshou and Changde counties. J Soochow University (Philosopy and social science Edition).2018; 39: 181–90.

  32. Chang J, Li J, Lu D, Zhu X, Lu C, Zhou Y, et al. The hydrological effect between Jingjiang River and Dongting Lake during the initial period of Three Gorges Project operation. J Geogr Sci. 2010;20:771–86.

    Article  Google Scholar 

  33. Li Z, Nie X, Zhang Y, Huang J, Huang B, Zeng G. Assessing the influence of water level on schistosomiasis in Dongting Lake region before and after the construction of Three Gorges Dam. Environ Monit Assess. 2016;188:28.

    Article  Google Scholar 

  34. Cheng G, Li D, Zhuang D, Wang Y. The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis. Acta Trop. 2016;164:194–207.

    Article  Google Scholar 

  35. Yang Y, Zhou Y, Song X, Li S, Zhong B, Wang T, et al. Integrated control strategy of schistosomiasis in the People’s Republic of China: projects involving agriculture, water conservancy, forestry, sanitation and environmental modification. Adv Parasit. 2016;92:237–68.

    Article  CAS  Google Scholar 

  36. Feng Z. The No. 1 flood of the Yangtze River in 2014 is forming in the middle reaches. In: chinanews. Ministry of Water Resources and Hydrology Bureau. 2014. Accessed 20 May 2020.

  37. Hunan Archives. Overview of Floods in Hunan.2006. Acceseed 12 August 2020.

  38. Zhang S, Wang T, Zhou Y, Cao Z, Zhang G, Wang N, et al. Influence of the Three Gorges Dam on schistosomiasis control in the middle and lower reaches of the Yangtze River. Glob Health J. 2019;3:9–15.

    Article  Google Scholar 

  39. Huang J, Liu K, Chang A, Peng J. Research on the response of the intermediate host-snail to the meteorological factors in Dongting Lake area. J Public Health Prevent Med. 2014;26:27–40.

    Google Scholar 

  40. Dai SM, Edwards J, Guan Z, Lv S, Li SZ, Zhang LJ, et al. Change patterns of oncomelanid snail burden in areas within the Yangtze River drainage after the Three Gorges Dam operated. Infect Dis Poverty. 2019;8:48.

    Article  Google Scholar 

  41. Lai X, Wang Z. Flood management of Dongting Lake after operation of Three Gorges Dam. Water Sci Eng. 2017;10:303–10.

    Article  Google Scholar 

  42. Yang Y, Zhou YB, Song XX. Integrated control strategy of schistosomiasis in The People’s Republic of China: Projects involving agriculture, water conservancy, forestry, sanitation and environmental modification. Adv Parasitol. 2016;92:237–68.

    Article  CAS  Google Scholar 

  43. Short EE, Caminade C, Thomas BN. Climate change contribution to the emergence or re-emergence of parasitic diseases. Infec Dis. 2017;10:102429384.

    Google Scholar 

  44. Chen Y, Liu J, Xiao Y, Zhong C, Wei F, Liu S. Spatiotemporal pattern analysis of schistosomiasis based on village level in the transmission control stage in lake and marshland areas in China. Parasitology. 2020;147:199–212.

    Article  Google Scholar 

  45. Tang Ling LHZJ. Analysis of the distribution status of and control strategies for snails in inner embankment of Hunan Province. J Trop Med Parasitol. 2016;14:192.

    Google Scholar 

Download references


The authors thank Dr. Ya Yang from Shanghai Sixth People's Hospital, who graduated from Fudan University, for his useful comments and advice, which have greatly improved the manuscript.


This research was funded by the National Science Foundation of China, grant no. 81673236. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results

Author information

Authors and Affiliations



SL, YS and YZ conceptualized the study. WD, HH and BH performed the data curation. CL, NZ, YZ, YW and SD analyzed the data. YS and SL wrote and prepared the original draft of the manuscript. YZ, YC, GR and QJ reviewed and edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yibiao Zhou.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, S., Shi, Y., Deng, W. et al. Spatio-temporal variations of emerging sites infested with schistosome-transmitting Oncomelania hupensis in Hunan Province, China, 1949–2016. Parasites Vectors 14, 7 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: