An integrated approach to identify distribution of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, in a mountainous region in China

The genetic diversity of Oncomelania hupensis robertsoni, intermediate hosts of Schistosoma japonicum in hilly regions of China, using microsatellite markers

BACKGROUND

The elimination of schistosomiasis remains a challenging task, with current measures primarily focused on the monitoring and control of Oncomelania hupensis (O. hupensis) snail, the sole intermediate host of Schistosome japonicum. Given the emerging, re-emerging, and persistent habitats of snails, understanding their genetic diversity might be essential for their successful monitoring and control. The aims of this study were to analyze the genetic diversity of Oncomelania hupensis robertsoni (O. h. robertsoni) using microsatellite DNA markers; and validate the applicability of previously identified microsatellite loci for O. hupensis in hilly regions.

METHODS

A total of 17 populations of O. h. robertsoni from Yunnan Province in China were selected for analysis of genetic diversity using six microsatellite DNA polymorphic loci (P82, P84, T4-22, T5-11, T5-13, and T6-27).

RESULTS

The number of alleles among populations ranged from 0 to 19, with an average of 5. The average ranges of expected (He) and observed (Ho) heterozygosity within populations were 0.506 to 0.761 and 0.443 to 0.792, respectively. The average fixation index within the population ranged from - 0.801 to 0.211. The average polymorphic information content (PIC) within the population ranged from 0.411 to 0.757, appearing to be polymorphic for all loci (all PIC > 0.5), except for P28 and P48. A total of 68 loci showed significant deviations from Hardy-Weinberg equilibrium (P < 0.05), and pairwise Fst values ranged from 0.051 to 0.379. The analysis of molecular variance indicated that 88% of the variation occurred within snail populations, whereas 12% occurred among snail populations. Phylogenetic trees and principal coordinate analysis revealed two distinct clusters within the snail population, corresponding to "Yunnan North" and "Yunnan South".

CONCLUSIONS

O. h. robertsoni exhibited a relatively high level of genetic differentiation, with variation chiefly existing within snail populations. All snail in this region could be separated into two clusters. The microsatellite loci P82 and P84 might not be suitable for classification studies of O. hupensis in hilly regions. These findings provided important information for the monitoring and control of snail, and for further genetic diversity studies on snail populations.

From Perpetual Wetness to Soil Chemistry: Enumerating Environmental and Physicochemical Factors Favoring Oncomelania hupensis quadrasi Snail Presence in the Municipality of Gonzaga, Cagayan, Philippines

Snail control to complement mass drug administration is being promoted by the World Health Organization for schistosomiasis control. Oncomelania hupensis quadrasi, the snail intermediate host of Schistosoma japonicum in the Philippines, has a very focal distribution; thus, scrutinizing baseline data and parameters affecting this distribution is very crucial. In this study in Gonzaga, Cagayan, Philippines, snail habitats were surveyed, and the various factors affecting the existence of the snails were determined. Malacological surveys and the mapping of sites of perpetual wetness in five endemic and five neighboring non-endemic barangays were conducted. Environmental and physicochemical factors were also examined. Maps of both snail and non-snail sites were generated. Of the fifty sites surveyed, O. h. quadrasi were found in twelve sites, and two sites yielded snails that were infected with S. japonicum cercariae. Factors such as silty loam soil, proximity to a snail site, water ammonia, and soil attributes (organic matter, iron, and pH) are all significantly associated with the presence of snails. In contrast, types of habitats, temperatures, and soil aggregation have no established association with the existence of snails. Mapping snail sites and determining factors favoring snail presence are vital to eliminating snails. These approaches will significantly maximize control impact and minimize wasted efforts and resources, especially in resource-limited schistosomiasis endemic areas.

Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models

BACKGROUND

Oncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control.

METHODS

Data describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585).

RESULTS

The RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change.

CONCLUSIONS

This study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.

Three Gorges Dam: the changing trend of snail density in the Yangtze River basin between 1990 and 2019

BACKGROUND

The area of Oncomelania hupensis snail remains around 3.6 billion m², with newly emerging and reemergent habitats continuing to appear in recent years. This study aimed to explore the long-term dynamics of snail density before and after the operation of Three Gorges Dam (TGD).

METHODS

Data of snail survey between 1990 and 2019 were collected from electronic databases and national schistosomiasis surveillance. Meta-analysis was conducted to estimate the snail density. Joinpoint model was used to identify the changing trend and inflection point. Inverse distance weighted interpolation (IDW) was used to determine the spatial distribution of recent snail density.

RESULTS

A total of 3777 snail survey sites with a precise location of village or beach were identified. For the downstream area, snail density peaked in 1998 (1.635/0.11 m², 95% CI: 1.220, 2.189) and fluctuated at a relatively high level before 2003, then declined steadily from 2003 to 2012. Snail density maintained lower than 0.150/0.11 m² between 2012 and 2019. Joinpoint model identified the inflection of 2003, and a significant decreasing trend from 2003 to 2012 with an annual percentage change (APC) being - 20.56% (95% CI: - 24.15, - 16.80). For the upstream area, snail density peaked in 2005 (0.760/0.11 m², 95% CI: 0.479, 1.207) and was generally greater than 0.300/0.11 m² before 2005. Snail density was generally lower than 0.150/0.11 m² after 2011. Snail density showed a significant decreasing trend from 1990 to 2019 with an APC being - 6.05% (95% CI: - 7.97, - 7.09), and no inflection was identified. IDW showed the areas with a high snail density existed in Poyang Lake, Dongting Lake, Jianghan Plain, and the Anhui branch of the Yangtze River between 2015 and 2019.

CONCLUSIONS

Snail density exhibited a fluctuating downward trend in the Yangtze River basin. In the downstream area, the operation of TGD accelerated the decline of snail density during the first decade period, then snail density fluctuated at a relatively low level. There still exists local areas with a high snail density. Long-term control and monitoring of snails need to be insisted on and strengthened.

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China

Background: As the unique intermediate host of Schistosoma japonicum, the geographical distribution of Oncomelania hupensis (O. hupensis) is an important index in the schistosomiasis surveillance system. This study comprehensively analyzed the pattern of snail distribution along the Yangtze River in Jiangsu Province and identified the dynamic determinants of the distribution of O. hupensis. Methods: Snail data from 2017 to 2021 in three cities (Nanjing, Zhenjiang, and Yangzhou) along the Yangtze River were obtained from the annual cross-sectional survey produced by the Jiangsu Institute of Parasitic Diseases. Spatial autocorrelation and hot-spot analysis were implemented to detect the spatio–temporal dynamics of O. hupensis distribution. Furthermore, 12 factors were used as independent variables to construct an ordinary least squares (OLS) model, a geographically weighted regression (GWR) model, and a geographically and temporally weighted regression (GTWR) model to identify the determinants of the distribution of O. hupensis. The adjusted coefficients of determination (adjusted R², AICc, RSS) were used to evaluate the performance of the models. Results: In general, the distribution of O. hupensis had significant spatial aggregation in the past five years, and the density of O. hupensis increased eastwards in the Jiangsu section of the lower reaches of the Yangtze River. Relatively speaking, the distribution of O. hupensis wase spatially clustered from 2017 to 2021, that is, it was found that the border between Yangzhou and Zhenjiang was the high density agglomeration area of O. hupensis snails. According to the GTWR model, the density of O. hupensis was related to the normalized difference vegetation index, wetness, dryness, land surface temperature, elevation, slope, and distance to nearest river, which had a good explanatory power for the snail data in Yangzhou City (adjusted R² = 0.7039, AICc = 29.10, RSS = 6.81). Conclusions: The distribution of O. hupensis and the environmental factors in the Jiangsu section of the lower reaches of the Yangtze River had significant spatial aggregation. In different areas, the determinants affecting the distribution of O. hupensis were different, which could provide a scientific basis for precise prevention and control of O. hupensis. A GTWR model was prepared and used to identify the dynamic determinants for the distribution of O. hupensis and contribute to the national programs of control of schistosomiasis and other snail-borne diseases.

Satellite Imagery-Based Identification of High-Risk Areas of Schistosome Intermediate Snail Hosts Spread after Flood

Snail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all inundation areas carry snails and may have overestimated snail spread areas. Furthermore, these studies only used a single environmental factor to estimate the snail survival risk probability, failing to analyze multiple variables, to accurately distinguish the snail survival risk in the snail spread areas. This paper proposes a systematic framework for early monitoring of snail diffusion to accurately map snail spread areas from remote sensing imagery and enhance snail survival risk probability estimation based on the snail spread map. In particular, the flooded areas are extracted using the Sentinel-1 Dual-Polarized Water Index based on synthetic aperture radar images to map all-weather flooding areas. These flood maps are used to extract snail spread areas, based on the assumption that only inundation areas that spatially interacted with (i.e., are close to) the previous snail distribution regions before flooding are identified as snail spread areas, in order to reduce the misclassification in snail spread area identification. A multiple logistic regression model is built to analyze how various types of snail-related environmental factors, including the normalized difference vegetation index (NDVI), wetness, river and channel density, and landscape fractal dimension impact snail survival, and estimate its risk probabilities in snail spread area. An experiment was conducted in Jianghan Plain, China, where snails are predominantly linearly distributed along the tributaries and water channels of the middle and lower reaches of the Yangtze River. The proposed method could accurately map floods under clouds, and a total area of 231.5 km2 was identified as the snail spread area. The snail survival risk probabilities were thus estimated. The proposed method showed a more refined snail spread area and a more reliable degree of snail survival risk compared with those of previous studies. Thus, it is an efficient way to accurately map all-weather snail spread and survival risk probabilities, which is helpful for schistosomiasis interruption.

Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China

Schistosomiasis is serious parasitic disease with an estimated global prevalence of active infections of more than 190 million. Accurate methods for the assessment of schistosomiasis risk are crucial for schistosomiasis prevention and control in China. Traditional approaches to the identification of epidemiological risk factors include pathogen biology, immunology, imaging, and molecular biology techniques. Identification of schistosomiasis risk has been revolutionized by the advent of computer network communication technologies, including 3S, mathematical modeling, big data, and artificial intelligence (AI). In this review, we analyze the development of traditional and new technologies for risk identification of schistosomiasis transmission in China. New technologies allow for the integration of environmental and socio-economic factors for accurate prediction of the risk population and regions. The combination of traditional and new techniques provides a foundation for the development of more effective approaches to accelerate the process of schistosomiasis elimination.

Genetic diversity and structure of Oncomelania hupensis hupensis in two eco-epidemiological settings as revealed by the mitochondrial COX1 gene sequences

BACKGROUND

Oncomelania hupensis hupensis is the only intermediate host of Schistosoma japonicum, the causative agent of schistosomiasis in China and is therefore of significant medical and veterinary health importance. Although tremendous progress has been achieved, there remains an understudied area of approximately 2.06 billion m² of potential snail habitats. This area could be further increased by annual flooding. Therefore, an understanding of population genetics of snails in these areas may be useful for future monitoring and control activities.

METHODS AND RESULTS

We sampled snails from Hexian (HX), Zongyang (ZY) and Shitai (ST) in Anhui (schistosomiasis transmission control), and from Hengtang (HT), Taicang (TC), Dongsan (DS) and Xisan (XS) in Jiangsu (schistosomiasis transmission interrupted), downstream of Anhui. ST, DS and XS are classified as hilly and mountainous areas, and HX, ZY, TC and HT as lake and marshland areas. The mitochondrial cytochrome c oxidase subunit I gene were sequenced. Out of 115 snails analyzed, 29 haplotypes were identified. We observed 56 (8.72%) polymorphic sites consisting of 51 transitions, four transversions and one multiple mutational change. The overall haplotype and nucleotide diversity were 0.899 and 0.01569, respectively. Snail populations in Anhui had higher genetic diversity than in Jiangsu. 73.32% of total variation was distributed among sites and 26.68% within sites. Snails were significantly separated according to eco-epidemiological settings in both network and phylogenetic analyses.

CONCLUSION

Our results could provide important guidance towards assessing coevolutionary interactions of snails with S. japonicum, as well as for future molluscan host monitoring and control activities.

Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

BACKGROUND

The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models.

METHODS

We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics.

RESULTS

Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease.

CONCLUSIONS

Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Drivers of Infection

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.

Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

Background

Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.

Methods

To delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.

Results

High values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R² = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.

Conclusions

The utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-3039-6) contains supplementary material, which is available to authorized users.

Spatial Distribution of Phlebotomus argentipes (Diptera: Psychodidae) in Eastern India, a Case Study Evaluating Multispatial Resolution Remotely Sensed Environmental Evidence and Microclimatic Data

Remote sensing, a powerful tool for analyzing landscape factors, is being used to explore the spatial ecology of vectors of several diseases. This study aims to explore the role of buffer size in identification and quantification of geo-environmental factors from multispatial resolution satellite data and its application along with microclimatic data to kala-azar vector abundance modeling.Sand fly abundance and microclimatic data were collected from 210 sample sites during the premonsoon and postmonsoon season of 2014 from Muzaffarpur district of Bihar (India). Linear imaging self-scanning sensor (LISS-III; 23.5 m) and advanced wide field sensor (AWiFS; 56 m) imageries were used for generating environmental variables at 300- and 500-m buffer zones. Four analytical models of sand fly density were developed and evaluated for predictive accuracy.A total of 33 geo-environmental and four microclimatic variables were tested for the prediction of sand fly density, of which the best four were maximum temperature, relative humidity, Euclidean nearest-neighbor distance of settlement area to mixed bush-grass land, and surface water body. Predictive accuracy of the LISS-III models was found to be higher than AWiFS models at all buffer sizes.The results show that geo-environmental parameters and microclimatic data are the best predictors for sand fly density modeling. Buffer sizes play an important role in identifying the explanatory variables. Model parameters may be useful in identifying predisposing factors of sand fly habitat suitability at the micro level.

Spatial analysis and identification of environmental risk factors affecting the distribution of Indoplanorbis and Lymnaea species in semi-arid and irrigated areas of Haryana, India

Fasciolosis, amphistomosis and schistosomosis, transmitted by the freshwater snail species Indoplanorbis and Lymnaea, are important snail-borne diseases in India as they affect the entire spectrum of domestic animals causing substantial mortality and economic loss. Identifying any heterogeneity in the spatial distribution of these snail-borne diseases will allow for targeted disease control and efficient use of resources. The objectives of this study were threefold: (i) to describe and explore the spatial distribution of Indoplanorbis and Lymnaea in Rohtak and Jhajjar districts of Haryana, India (ii) to identify factors associated with occurrence of these freshwater snail species and (iii) to produce a map showing the predicted risk of occurrence of Lymnaea and Indoplanorbis spp. in the study area. Snails were collected from water bodies of 99 settlements out of a total of 453 in the study area. Kernel smoothing was used to generate a kernel ratio map while Kulldorff's spatial scan statistic was used to detect clusters of settlements with a high/low risk. Multivariable logistic regression showed that snails were almost ten times more likely to be present in rice-growing areas than in those not growing rice (OR 9.24) and that snails were less likely to be present with each 1 km increase in distance from a canal (OR 0.86). The regression model was used to produce a map illustrating the predicted risk of snail occurrence. Since the distribution of vector snails mirrors the distribution of snail-borne parasitic diseases, such spatial analysis helps to determine the relative risk of snail-infestation as well as snail-borne diseases' distribution and planning of control activities.

Reaching the Surveillance-Response Stage of Schistosomiasis Control in The People's Republic of China: A Modelling Approach

With the goal set to eliminate schistosomiasis nationwide by 2020, The People's Republic of China has initiated the surveillance-response stage to identify remaining sources of infection and potential pockets from where the disease could reemerge. Shifting the focus from classical monitoring and evaluation to rapid detection and immediate response, this approach requires modelling to bridge the surveillance and response components. We review here studies relevant to schistosomiasis modelling in a Chinese surveillance-response system with the expectation to achieve a practically useful understanding of the current situation and potential future study directions. We also present useful experience that could tentatively be applied in other endemic regions in the world. Modelling is discussed at length as it plays an essential role, both with regard to the intermediate snail host and in the definitive, mammal hosts. Research gaps with respect to snail infection, animal hosts and sectoral research cooperation are identified and examined against the prevailing background of ecosystem and socioeconomic changes with a focus on coexisting challenges and opportunities in a situation with increasing financial constraints.

Applications of Spatial Technology in Schistosomiasis Control Programme in The People's Republic of China

Schistosomiasis, as the important parasitic disease, has caused serious threats to human health globally. The People's Republic of China has acquired significant achievements based on large-scale interventions and innovational technology. The spatial technology was introduced in 1980s and widely used in the study and control of schistosomiasis in The People's Republic of China. This chapter reviews the progress and application of spatial technology in schistosomiasis control by analysing the spatiotemporal pattern of and the impact of ecological changes on schistosomiasis transmission, which have provided the information to design and select the control strategy, and assisted the establishment of the monitoring and early warning system in The People's Republic of China, especially in the marshland and mountainous regions.

Biology and Control of Snail Intermediate Host of Schistosoma japonicum in The People's Republic of China

Schistosomiasis caused by Schistosoma japonicum is a severe parasitic disease in The People's Republic of China and imposed considerable burden on human and domestic animal health and socioeconomic development. The significant achievement in schistosomiasis control has been made in last 60years. Oncomelania hupensis as the only intermediate host of S. japonicum plays a key role in disease transmission. The habitat complexity of the snails challenges to effective control. In this review we share the experiences in control and research of O. hupensis.

Ecological Model to Predict Potential Habitats of Oncomelania hupensis, the Intermediate Host of Schistosoma japonicum in the Mountainous Regions, China

Background

Schistosomiasis japonica is a parasitic disease that remains endemic in seven provinces in the People’s Republic of China (P.R. China). One of the most important measures in the process of schistosomiasis elimination in P.R. China is control of Oncomelania hupensis, the unique intermediate host snail of Schistosoma japonicum. Compared with plains/swamp and lake regions, the hilly/mountainous regions of schistosomiasis endemic areas are more complicated, which makes the snail survey difficult to conduct precisely and efficiently. There is a pressing call to identify the snail habitats of mountainous regions in an efficient and cost-effective manner.

Methods

Twelve out of 56 administrative villages distributed with O. hupensis in Eryuan, Yunnan Province, were randomly selected to set up the ecological model. Thirty out of the rest of 78 villages (villages selected for building model were excluded from the villages for validation) in Eryuan and 30 out of 89 villages in Midu, Yunnan Province were selected via a chessboard method for model validation, respectively. Nine-year-average Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) as well as Digital Elevation Model (DEM) covering Eryuan and Midu were extracted from MODIS and ASTER satellite images, respectively. Slope, elevation and the distance from every village to its nearest stream were derived from DEM. Suitable survival environment conditions for snails were defined by comparing historical snail presence data and remote sensing derived images. According to the suitable conditions for snails, environment factors, i.e. NDVI, LST, elevation, slope and the distance from every village to its nearest stream, were integrated into an ecological niche model to predict O. hupensis potential habitats in Eryuan and Midu. The evaluation of the model was assessed by comparing the model prediction and field investigation. Then, the consistency rate of model validation was calculated in Eryuan and Midu Counties, respectively.

Results

The final ecological niche model for potential O. hupensis habitats prediction comprised the following environmental factors, namely: NDVI (≥ 0.446), LST (≥ 22.70°C), elevation (≤ 2,300 m), slope (≤ 11°) and the distance to nearest stream (≤ 1,000 m). The potential O. hupensis habitats in Eryuan distributed in the Lancang River basin and O. hupensis in Midu shows a trend of clustering in the north and spotty distribution in the south. The consistency rates of the ecological niche model in Eryuan and Midu were 76.67% and 83.33%, respectively.

Conclusions

The ecological niche model integrated with NDVI, LST, elevation, slope and distance from every village to its nearest stream adequately predicted the snail habitats in the mountainous regions.

Schistosomiasis japonica is a parasitic disease caused by the infection of Schistosoma japonicum. Oncomelania hupensis, serving as the unique intermediate host of S. japonicum, has a distribution highly correlated with schistosomiasis epidemic. At present, elimination of O. hupensis is still an important target for disease control in the People’s Republic of China. In mountainous regions, compared with two other endemic regions, snails are hard to detect due to the complicated environmental conditions and poor transportation systems. In this study, we developed an ecological niche model to predict the potential habitats of O. hupensis using remote sensing data including vegetation index, land surface temperature, elevation, slope and the distance from every village to its nearest stream. Validation of the approach was performed in two counties with similar ecological conditions in Yunnan Province, P.R. China. Results revealed a model with a good consistency rate of 76.67% and 83.33% for the two counties, respectively. The model holds promise for snail surveillance in mountainous regions.

Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China

Background

Schistosomiasis japonica still remains of public health and economic significance in China, especially in the lake and marshland areas along the Yangtze River Basin, where the control of transmission has proven difficult. In the study, we investigated spatio-temporal variations of S. japonicum infection risk in Anhui Province and assessed the associations of the disease with key environmental factors with the aim of understanding the mechanism of the disease and seeking clues to effective and sustainable schistosomiasis control.

Methodology/Principal Findings

Infection data of schistosomiasis from annual conventional surveys were obtained at the village level in Anhui Province, China, from 2000 to 2010 and used in combination with environmental data. The spatio-temporal kriging model was used to assess how these environmental factors affected the spatio-temporal pattern of schistosomiasis risk. Our results suggested that seasonal variation of the normalized difference vegetation index (NDVI), seasonal variation of land surface temperature at daytime (LSTD), and distance to the Yangtze River were negatively significantly associated with risk of schistosomiasis. Predictive maps showed that schistosomiasis prevalence remained at a low level and schistosomiasis risk mainly evolved along the Yangtze River. Schistosomiasis risk also followed a focal spatial pattern, fluctuating temporally with a peak (the largest spatial extent) in 2005 and then contracting gradually but with a scattered distribution until 2010.

Conclusion

The fitted spatio-temporal kriging model can capture variations of schistosomiasis risk over space and time. Combined with techniques of geographic information system (GIS) and remote sensing (RS), this approach facilitates and enriches risk modeling of schistosomiasis, which in turn helps to identify prior areas for effective and sustainable control of schistosomiasis in Anhui Province and perhaps elsewhere in China.

Schistosomiasis japonica is one of the most serious parasitic diseases in China. It is estimated that more than 50 million people are still at risk, especially those living in the lake and marshland areas along the Yangtze River Basin. The Chinese government has made great efforts to implement schistosomiasis control programs since 1950s. The latest, major two programs are the 10-year World Bank Loan Project (WBLP) terminated in 2001, which was based on large-scale chemotherapy, and the national integrated control strategy implemented since 2005, which was aimed at reducing the roles of bovines and humans as infection sources. Based on spatio-temporal analyses of the S. japonicum infection prevalence data during 2000–2010 in Anhui Province, we found schistosomiasis prevalence remained at a low level but the spatial distribution of the disease became widely scattered at the later stage of the study period, suggesting that the integrated program could not fully effectively reduce the spatial extent of schistosomiasis risk. To achieve an effective and sustainable control strategy, we emphasize the need to control snail habitats within areas of high schistosomiasis risk.

Eco-social determinants of Schistosoma japonicum infection supported by multi-level modelling in Eryuan county, People's Republic of China

Schistosomiasis remains of considerable public health concern in many tropical and subtropical regions of the world, including the People's Republic of China (P.R. China). The effectiveness of schistosomiasis control interventions are, among other factors, governed by the social-ecological context. However, eco-social determinants of schistosomiasis are poorly understood, particularly at the household or village levels. In the current study, residents in 26 villages of Eryuan county, Yunnan province, P.R. China, were screened for Schistosoma japonicum infection with a serological assay that was followed by stool examination for sero-positive individuals. Bayesian multilevel models with spatial random effects were employed to profile the S. japonicum infection risk based on known transmission sites of S. japonicum that are scattered across individual land parcels in this part of the country. The key risk factors identified with this approach were the absence of a sanitary stall house for livestock and presence of living and infected intermediate host snails in close proximity. We conclude that a spatially explicit Bayesian multilevel approach can deepen our understanding of eco-social determinants that govern schistosomiasis transmission at a small geographical scale.

Identifying determinants of Oncomelania hupensis habitats and assessing the effects of environmental control strategies in the plain regions with the waterway network of China at the microscale

This study aims to identify the landscape ecological determinants related to Oncomelania hupensis distribution, map the potential high risk of O. hupensis habitats at the microscale, and assess the effects of two environmental control strategies. Sampling was performed on 242 snail sites and 726 non-snail sites throughout Qianjiang City, Hubei Province, China. An integrated approach of landscape pattern analysis coupled with multiple logistic regression modeling was applied to investigate the effects of environmental factors on snail habitats. The risk probability of snail habitats positively correlated with patch fractal dimension (FD), paddy farm land proportion, and wetness index but inversely correlated with categorized normalized difference vegetation index (NDVI) and elevation. These findings indicate that FD can identify irregular features (e.g., irrigation ditches) in plain regions and that a moderate NDVI increases the microscale risk probability. Basing on the observed determinants, we predicted a map showing high-risk areas of snail habitats and simulated the effects of conduit hardening and paddy farming land rotation to dry farming land. The two approaches were confirmed effective for snail control. These findings provide an empirical basis for health professionals in local schistosomiasis control stations to identify priority areas and promising environmental control strategies for snail control and prevention.

Establishing and applying a schistosomiasis early warning index (SEWI) in the lower Yangtze River Region of Jiangsu Province, China

Background

China has made remarkable progress in schistosomiasis control over the past decades. Transmission control has replaced morbidity control as the country moves towards the goal of elimination and the current challenge is to find a sensitive measure capable of gauging transmission risk in low-prevalence areas. The study aims to develop a Schistosomiasis Early Warning Index (SEWI) and demonstrate its use in Jiangsu Province along the lower Yangtze River.

Methodology/Principal Findings

The Delphi approach, a structured communication technique, was used to develop the SEWI. Two rounds of interviews with 30 public health experts specialized in schistosomiasis control were conducted using 40 indicators that reflected different aspects of schistosomiasis transmission and control. The necessity, feasibility, and sensitivity of each indicator were assessed and the weight value of each indicator determined based on these experts' judgment. The system included 3 first-order indicators, 7 second-order indicators, and 30 third-order indicators. The 3 first-order indicators were endemic status, control measures, social and environmental factors, with the weight values 0.366, 0.343 and 0.291, respectively. For the 7 second-order indicators, the highest weight value was for control measures for snails (0.175) and the lowest for transmission route (0.110). We estimated and mapped the SEWI for endemic areas at the county scale in Jiangsu Province finding that the majority of the endemic areas were characterized as medium transmission risk (SEWI risk values between 0.3 and 0.6), while areas where transmission interruption had been officially declared showed SEWI values <0.30. A few isolated areas (e.g. endemic islands in the Yangtze River) produced SEWI values >0.60. These estimates are largely in agreement with the endemicity levels based on recent epidemiological surveys.

Conclusions/Significance

The SEWI should be useful for estimation of schistosomiasis transmission surveillance, particularly with reference to the elimination of the disease in China.

Spatio-temporal analysis to identify determinants of Oncomelania hupensis infection with Schistosoma japonicum in Jiangsu province, China

Background

With the successful implementation of integrated measures for schistosomiasis japonica control, Jiangsu province has reached low-endemicity status. However, infected Oncomelania hupensis snails could still be found in certain locations along the Yangtze river until 2009, and there is concern that they might spread again, resulting in the possible re-emergence of infections among people and domestic animals alike. In order to establish a robust surveillance system that is able to detect the spread of infected snails at an early stage, sensitive and reliable methods to identify risk factors for the establishment of infected snails need to be developed.

Methods

A total of 107 villages reporting the persistent presence of infected snails were selected. Relevant data on the distribution of infected snails, and human and livestock infection status information for the years 2003 to 2008 were collected. Spatio-temporal pattern analysis including spatial autocorrelation, directional distribution and spatial error models were carried out to explore spatial correlations between infected snails and selected explanatory factors.

Results

The area where infected snails were found, as well as their density, decreased significantly between 2003 and 2008. Changes in human and livestock prevalences were less pronounced. Three statistically significant spatial autocorrelations for infected snails were identified. (i) The Moran’s I of infected snails increased from 2004 to 2007, with the snail density increasing and the area with infected snails decreasing. (ii) The standard deviations of ellipses around infected snails were decreasing and the central points of the ellipses moved from West to East. (iii) The spatial error models indicated no significant correlation between the density of infected snails and selected risk factors.

Conclusions

We conclude that the contribution of local infection sources including humans and livestock to the distribution of infected snails might be relatively small and that snail control may limit infected snails to increasingly small areas ecologically most suitable for transmission. We provide a method to identify these areas and risk factors for persistent infected snail presence through spatio-temporal analysis, and a suggested framework, which could assist in designing evidence based control strategies for schistosomiasis japonica elimination.

Remote sensing and disease control in China: past, present and future

Satellite measurements have distinct advantages over conventional ground measurements because they can collect the information repeatedly and automatically. Since 1970 globally and 1985 in China, the availability of remote sensing (RS) techniques has steadily grown and they are becoming increasingly important to improve our understanding of human health. This paper gives the first detailed overview on the developments of RS applications for disease control in China. The problems, challenges and future directions are also discussed with an aim of guiding prospective studies.

A specific PCR assay for the identification and differentiation of Schistosoma japonicum geographical isolates in mainland China based on analysis of mitochondrial genome sequences

In the present study, near-complete mt genome sequences for eight representative Schistosoma japonicum samples from seven endemic provinces in mainland China were analyzed. Sequence differences among the eight mt genomes of S. japonicum samples were 0.20-2.51%. Variation in protein-coding genes was greater than that in rRNA genes. The mt DNA sequences of S. japonicum samples from south-western (SW) China were 2 bp [position 11727-11728 within tRNA-Cys, microsatellite (AG) indel] longer than those of the parasites from the lower Yangtze/Zhejiang areas. Representative DNA sequencing confirmed that such (AG) indel could be exploited for identification and differentiation of S. japonicum populations in SW China's Yunnan and Sichuan province which have two (AG) repeats from those in all remaining endemic provinces along the Yangtze River below the Three Gorges regions or close to the east coast of China (e.g., Zhejiang) which have only one (AG) repeat. Phylogenetic analyses based on the concatenated amino acids of 12 protein-coding genes also showed that samples from SW China (Sichuan and Yunnan provinces), above the Three Gorges Dam, formed a distinct cluster. Based on this indel polymorphism, a pair of specific primers was designed and used to develop a specific-PCR polyacrylamide gel detection assay. There was an obvious length difference in the amplified PCR products between S. japonicum samples from the two endemic types. The specific-PCR assay allowed the specific identification of S. japonicum, with no amplicons being amplified from other closely related trematodes, and the minimum amount of DNA detectable was 0.05 ng. This approach is inexpensive, easy to perform and the whole detection process can be completed within 4h. Examination of 81 S. japonicum samples from SW China's Yunnan and Sichuan provinces, and 264 samples from the lower Yangtze provinces (Hubei, Jiangsu, Jiangxi, Anhui and Hunan) and from Zhejiang validated the value of the specific PCR assay and proved its reliability. These findings indicate that the specific PCR assay would provide a useful tool for the epidemiological surveillance and for tracing the source of S. japonicum infection in humans and animals in China.

Location of active transmission sites of Schistosoma japonicum in lake and marshland regions in China

Schistosomiasis control in China has, in general, been very successful during the past several decades. However, the rebounding of the epidemic situation in some areas in recent years raises concerns about a sustainable control strategy of which locating active transmission sites (ATS) is a necessary first step. This study presents a systematic approach for locating schistosomiasis ATS by combining the approaches of identifying high risk regions for schisotosmiasis and extracting snail habitats. Environmental, topographical, and human behavioural factors were included in the model. Four significant high-risk regions were detected and 6 ATS were located. We used the normalized difference water index (NDWI) combined with the normalized difference vegetation index (NDVI) to extract snail habitats, and the pointwise 'P-value surface' approach to test statistical significance of predicted disease risk. We found complicated non-linear relationships between predictors and schistosomiasis risk, which might result in serious biases if data were not properly treated. We also found that the associations were related to spatial scales, indicating that a well-designed series of studies were needed to relate the disease risk with predictors across various study scales. Our approach provides a useful tool, especially in the field of vector-borne or environment-related diseases.

Spatial epidemiology in zoonotic parasitic diseases: insights gained at the 1st International Symposium on Geospatial Health in Lijiang, China, 2007

The 1st International Symposium on Geospatial Health was convened in Lijiang, Yunnan province, People's Republic of China from 8 to 9 September, 2007. The objective was to review progress made with the application of spatial techniques on zoonotic parasitic diseases, particularly in Southeast Asia. The symposium featured 71 presentations covering soil-transmitted and water-borne helminth infections, as well as arthropod-borne diseases such as leishmaniasis, malaria and lymphatic filariasis. The work made public at this occasion is briefly summarized here to highlight the advances made and to put forth research priorities in this area. Approaches such as geographical information systems (GIS), global positioning systems (GPS) and remote sensing (RS), including spatial statistics, web-based GIS and map visualization of field investigations, figured prominently in the presentation.