Remote sensing and spatial statistical analysis to predict the distribution of Oncomelania hupensis in the marshlands of China

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.

Spectral responses in rangelands and land cover change by livestock in regions of the Caatinga biome, Brazil

This study aimed to analyze fragments of rangelands through spectral responses and land cover change by livestock in regions of the Caatinga biome through remote sensing. For spectral behavior, the surface reflectance bidirectional (SRB) and spectral indexes of vegetation were used to verify the ragelands seasonality. Land cover change detection of Ouricuri and Tauá through Landsat-8 images with a 16-day revisit interval, were processed in the Google Earth Engine platform (GEE) and software Quantum GIS version 2.18 (QGIS). In the GEE platform, annual mosaics and stacking of the spectral bands were generated for the classification of images, and in sequence the production of thematic maps in QGIS. The analysis of land cover change considered the classes: thinned Caatinga, conserved Caatinga, herbaceous vegetation, bare soil, water and others. The analysis of the spectral responses showed that the vegetation monitored in Ouricuri presented higher SRB in the infrared band and lower SRB in the red and blue bands, and that caused the pasture to produce higher vegetation indexes than the other locations. Through validation, it was observed that in Tauá, there was an overall accuracy of 91% and Kappa index of 89%, and in Ouricuri there was an overall accuracy of 90% and Kappa index of 86%, indicating excellent correctness of the classification model. The classification model proved to be effective in verifying the temporal and spatial land cover change, making it possible to identify places with the vegetation that was most affected and susceptible to degradation and generation of political support to minimize damage to the Caatinga Biome.

Infestation risk of the intermediate snail host of Schistosoma japonicum in the Yangtze River Basin: improved results by spatial reassessment and a random forest approach

BACKGROUND

Oncomelania hupensis is only intermediate snail host of Schistosoma japonicum, and distribution of O. hupensis is an important indicator for the surveillance of schistosomiasis. This study explored the feasibility of a random forest algorithm weighted by spatial distance for risk prediction of schistosomiasis distribution in the Yangtze River Basin in China, with the aim to produce an improved precision reference for the national schistosomiasis control programme by reducing the number of snail survey sites without losing predictive accuracy.

METHODS

The snail presence and absence records were collected from Anhui, Hunan, Hubei, Jiangxi and Jiangsu provinces in 2018. A machine learning of random forest algorithm based on a set of environmental and climatic variables was developed to predict the breeding sites of the O. hupensis intermediated snail host of S. japonicum. Different spatial sizes of a hexagonal grid system were compared to estimate the need for required snail sampling sites. The predictive accuracy related to geographic distances between snail sampling sites was estimated by calculating Kappa and the area under the curve (AUC).

RESULTS

The highest accuracy (AUC = 0.889 and Kappa = 0.618) was achieved at the 5 km distance weight. The five factors with the strongest correlation to O. hupensis infestation probability were: (1) distance to lake (48.9%), (2) distance to river (36.6%), (3) isothermality (29.5%), (4) mean daily difference in temperature (28.1%), and (5) altitude (26.0%). The risk map showed that areas characterized by snail infestation were mainly located along the Yangtze River, with the highest probability in the dividing, slow-flowing river arms in the middle and lower reaches of the Yangtze River in Anhui, followed by areas near the shores of China's two main lakes, the Dongting Lake in Hunan and Hubei and the Poyang Lake in Jiangxi.

CONCLUSIONS

Applying the machine learning of random forest algorithm made it feasible to precisely predict snail infestation probability, an approach that could improve the sensitivity of the Chinese schistosome surveillance system. Redesign of the snail surveillance system by spatial bias correction of O. hupensis infestation in the Yangtze River Basin to reduce the number of sites required to investigate from 2369 to 1747.

Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches

Elimination of the intermediate snail host of Schistosoma is the most effective way to control schistosomiasis and the most important first step is to accurately identify the snail habitats. Due to the substantial resources required for traditional, manual snail-searching in the field, and potential risk of miss-classification of potential snail habitats by remote sensing, more convenient and precise methods are urgently needed. Snail data (N = 15,000) from two types of snail habitats (lake/marshland and hilly areas) in Anhui Province, a typical endemic area for schistosomiasis, were collected together with 36 environmental variables covering the whole province. Twelve different models were built and evaluated with indices, such as area under the curve (AUC), Kappa, percent correctly classified (PCC), sensitivity and specificity. We found the presence-absence models performing better than those based on presence-only. However, those derived from machine-learning, especially the random forest (RF) approach were preferable with all indices above 0.90. Distance to nearest river was found to be the most important variable for the lake/marshlands, while the climatic variables were more important for the hilly endemic areas. The predicted high-risk areas for potential snail habitats of the lake/marshland type exist mainly along the Yangtze River, while those of the hilly type are dispersed in the areas south of the Yangtze River. We provide here the first comprehensive risk profile of potential snail habitats based on precise examinations revealing the true distribution and habitat type, thereby improving efficiency and accuracy of snail control including better allocation of limited health resources.

High-resolution remote sensing-based spatial modeling for the prediction of potential risk areas of schistosomiasis in the Dongting Lake area, China

The geographical distribution of snail (i.e., the intermediate host of schistosomiasis) is consistent with that of endemic areas. The suitable snail habitus requires necessary environmental conditions for snail population. The high-resolution remote sensing provides an important tool for the spatio-temporal analysis of disease monitoring and prediction. This study conducted a typical schistosomiasis epidemic area in the marshland and lake regions along the Yangtze River, Yueyang City, Hunan Province of China. And three types of environmental factors, i.e., NDVI, soil moisture, and shortest distance to water body, associated with the geographical distribution of snail population, were extracted from the high-resolution remoting sensing data. The predicted distribution of snail habitus from the high-resolution environmental factors were compared with the data of annual program of snail survey. The results have shown that the application of high-resolution remote sensing can improve the accuracy of the modeled and predicted the potential risk areas of schistosomiasis, and may become an important tool for the ongoing national schistosomiasis control program.

High-resolution remote sensing-based spatial modeling for the prediction of potential risk areas of schistosomiasis in the Dongting Lake area, China

The geographical distribution of snail (i.e., the intermediate host of schistosomiasis) is consistent with that of endemic areas. The suitable snail habitus requires necessary environmental conditions for snail population. The high-resolution remote sensing provides an important tool for the spatio-temporal analysis of disease monitoring and prediction. This study conducted a typical schistosomiasis epidemic area in the marshland and lake regions along the Yangtze River, Yueyang City, Hunan Province of China. And three types of environmental factors, i.e., NDVI, soil moisture, and shortest distance to water body, associated with the geographical distribution of snail population, were extracted from the high-resolution remoting sensing data. The predicted distribution of snail habitus from the high-resolution environmental factors were compared with the data of annual program of snail survey. The results have shown that the application of high-resolution remote sensing can improve the accuracy of the modeled and predicted the potential risk areas of schistosomiasis, and may become an important tool for the ongoing national schistosomiasis control program.

Identification of Potential High-Risk Habitats within the Transmission Reach of Oncomelania hupensis after Floods Based on SAR Techniques in a Plane Region in China

Schistosomiasis japonica is an infectious disease caused by Schistosoma japonicum, and it remains endemic in China. Flooding is the main hazard factor, as it causes the spread of Oncomelania hupensis, the only intermediate host of Schistosoma japonicum, thereby triggering schistosomiasis outbreaks. Based on multi-source real-time remote sensing data, we used remote sensing (RS) technology, especially synthetic aperture radar (SAR), and geographic information system (GIS) techniques to carry out warning research on potential snail habitats within the snail dispersal range following flooding. Our research result demonstrated: (1) SAR data from Sentinel-1A before and during a flood were used to identify submerged areas rapidly and effectively; (2) the likelihood of snail survival was positively correlated with the clay proportion, core area standard deviation, and ditch length but negatively correlated with the wetness index, NDVI (normalized difference vegetation index), elevation, woodland area, and construction land area; (3) the snail habitats were most abundant near rivers and ditches in paddy fields; (4) the rivers and paddy irrigation ditches in the submerged areas must be the focused of mitigation efforts following future floods.

Evolution of the National Schistosomiasis Control Programmes in The People's Republic of China

Schistosomiasis japonica is caused by the parasitic trematode Schistosoma japonicum. It is endemic in The People's Republic of China and has significant impact on human health and socioeconomic development in certain regions. Over the last six decades, the national control programmes evolved in remarkable ways and brought schistosomiasis japonica largely under control. We describe the history and evolution of schistosomiasis control in The People's Republic of China, with an emphasis on shifts in control strategies that evolved with new insights into the biology of the parasite and its intermediate hosts, and the epidemiology of the disease in the country. We also highlight the achievements in controlling the disease in different socioecological settings, and identify persisting challenges to fully eliminate schistosomiasis japonica from the country. To reach the goal of schistosomiasis elimination, further integration of interventions, multisector collaboration, sensitive and effective surveillance are needed to strengthen.

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.

China-Africa and China-Asia Collaboration on Schistosomiasis Control: A SWOT Analysis

Schistosomiasis, a disease caused by a trematode, parasitic worm, is a worldwide public health problem. In spite of great progress with regard to morbidity control, even elimination of this infection in recent decades, there are still challenges to overcome in sub-Saharan Africa and endemic areas in Southeast Asia. Regarded as one of the most successful countries with respect to schistosomiasis control, The People's Republic of China has accumulated considerable experience and learnt important lessons in various local settings that could benefit schistosomiasis control in other endemic countries. Based on an analysis of conceived strengths, weaknesses, opportunities and threats (SWOT) of potential collaborative activities with regard to schistosomiasis in Africa and Asia, this article addresses the importance of collaborative efforts and explores the priorities that would be expected to facilitate the transfer of Chinese experience to low- and middle-income countries in Africa and Asia.

Schistosomiasis japonica: modelling as a tool to explore transmission patterns

Modelling is an important tool for the exploration of Schistosoma japonicum transmission patterns. It provides a general theoretical framework for decision-makers and lends itself specifically to assessing the progress of the national control programme by following the outcome of surveys. The challenge of keeping up with the many changes of social, ecological and environmental factors involved in control activities is greatly facilitated by modelling that can also indicate which activities are critical and which are less important. This review examines the application of modelling tools in the epidemiological study of schistosomiasis japonica during the last 20 years and explores the application of enhanced models for surveillance and response. Updated and timely information for decision-makers in the national elimination programme is provided but, in spite of the new modelling techniques introduced, many questions remain. Issues on application of modelling are discussed with the view to improve the current situation with respect to schistosomiasis japonica.

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.

Identification of parasite-host habitats in Anxiang county, Hunan Province, China based on multi-temporal China-Brazil earth resources satellite (CBERS) images

Remote sensing is a promising technique for monitoring the distribution and dynamics of various vector-borne diseases. In this study, we used the multi-temporal CBERS images, obtained free of charge, to predict the habitats of the snail Oncomelania hupensis, the sole intermediate host of schistosomiasis japonica, a snail-borne parasitic disease of considerable public health in China. Areas of suitable snail habitats were identified based on the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), and the predictive model was tested against sites (snails present or absent) that were surveyed directly for O. hupensis. The model performed well (sensitivity and specificity were 63.64% and 78.09%, respectively), and with further development, we may provide an accurate, inexpensive tool for the broad-scale monitoring and control of schistosomiasis, and other similar vector-borne diseases.

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.

Coordinating research on neglected parasitic diseases in Southeast Asia through networking

The new dialogue between stakeholders, that is, scientists, research administrators and donors as well as the populations victimized by endemic infections, is initiating a virtuous circle leading to lower disease-burdens, improved public health and the mitigation of poverty. There is now general agreement that control activities need research collaboration to advance, while surveillance plays an increasingly important role in sustaining long-term relief. On the part of the Regional Network on Asian Schistosomiasis and Other Helminth Zoonoses (RNAS(+)), this has led to a new vision not only focused on general strengthening of research capabilities but also on furthering efforts to close the gap between research and control and bridge different branches of science. From its original, exclusive focus on schistosomiasis, RNAS(+) has expanded to include food-borne and soil-transmitted helminth infections as well. Its current repository of data on the distribution, prevalence and severity of these diseases is increasingly utilised by decision makers charged with epidemiological control in the endemic countries. Thanks to a more rapid translation of research results into control applications and the dissemination of data and new technology through networking, the overall situation is improving. Working as a virtual organisation of researchers and control officers in the endemic countries of Southeast Asia, RNAS(+) is playing an important role in this conversion. Its responsibilities are divided along disease lines into five main areas, but no serious, endemic disease is considered to be outside the network's sphere of interest. This chapter recounts some of the more important RNAS(+) accomplishments, pinpoints potential directions for future operations and highlights areas where research is most needed.

Health research based on geospatial tools: a timely approach in a changing environment

The possibilities of disease prediction based on the environmental characteristics of geographical areas and specific requirements of the causative infectious agents are reviewed and, in the case of parasites whose life cycles involve more than one host, the needs of the intermediate hosts are also referred to. The geographical information systems framework includes epidemiological data, visualization (in the form of maps), modelling and exploratory analysis using spatial statistics. Examples include climate-based forecast systems, based on the concept of growing degree days, which now exist for several parasitic helminths such as fasciolosis, schistosomiasis, dirofilariasis and also for malaria. The paper discusses the limits of data collection by remote sensing in terms of resolution capabilities (spatial, temporal and spectral) of sensors on-board satellites. Although the data gained from the observation of oceans, land, elevations, land cover, land use, surface temperatures, rainfall, etc. are primarily for weather forecasting, military and commercial use, some of this information, particularly that from the climate research satellites, is of direct epidemiological utility. Disease surveillance systems and early-warning systems (EWS) are prime examples of academic approaches of practical importance. However, even commercial activities such as the construction of virtual globes, i.e. computer-based models of the Earth, have been used in this respect. Compared to conventional world maps, they do not only show geographical and man-made features, but can also be spatially annotated with data on disease distribution, demography, economy and other measures of particular interest.

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.

Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China

Schistosomiasis control in China is facing a new challenge due to the rebound of epidemics in many areas and the unsustainable effects of the chemotherapy-based control strategy. Identifying high-risk regions for schistosomiasis is an important first step for an effective and sustainable strategy. Direct surveillance of snail habitats to detect high-risk regions is costly and no longer a desirable approach, while indirect monitoring of acute schistosomiasis may be a satisfactory alternative. To identify high-risk regions for schistosomiasis, we jointly used multiplicative and additive models with the kernel smoothing technique as the main approach to estimate the relative risk (RR) and excess risk (ER) surfaces by analyzing surveillance data for acute schistosomiasis. The feasibility of detecting high-risk regions for schistosomiasis through nonparametric spatial analysis was explored and confirmed in this study, and two significant high-risk regions were identified. The results provide useful hints for improving the national surveillance network for acute schistosomiasis and possible approaches to utilizing surveillance data more efficiently. In addition, the commonly used epidemiological indices, RR and ER, are examined and emphasized from the spatial point of view, which will be helpful for exploring many other epidemiological indices.

Identifying high-risk regions for schistosomiasis in Guichi, China: a spatial analysis

Schistosomiasis epidemic is reemerging in some areas of China. The extensive snail habitat is a major challenge for a sustainable schistosomiasis control. Direct surveillance on snails for the disease control is no longer a desirable disease control approach due to current low density of infected snails and reduced funding. In this study the benefit of indirect monitoring of acute schistosomiasis cases, using spatial methods including disease mapping and spatial clustering analysis was explored in Guichi, China. Significant global clustering existed for acute cases and two statistically significant spatial clusters were detected, and subsequently validated by field surveys. Our study indicates that the application of geographic information system (GIS) and spatial methods are useful in the epidemiologic surveillance and risk assessment for acute schistosomiasis, providing an alternative approach with minimal funds required.

Hydrological modelling of snail dispersal patterns in Msambweni, Kenya and potential resurgence of Schistosoma haematobium transmission

Urinary schistosomiasis is an important source of human morbidity in Msambweni, Kenya, where the intermediate host snail, Bulinus nasutus is found in ponds and water pools. In the past, aquatic habitats in the area have been studied separately; however, recent collections of B. nasutus snails and shells indicated that many of these ponds are in fact connected during and following sufficient rains. Satellite imagery and a geographical information system (GIS) were used to survey the main water courses and potential drainage routes, to locate potential source populations of snails and to determine probable snail dispersal routes. The 2 water bodies implicated as being the most important Schistosoma haematobium transmission foci in the area were found to differ in their degree of connectivity to other B. nasutus source habitats. One pond becomes connected even after normal rains, while the other pond requires prolonged rains or flooding to become connected with source habitats. Consequently, the transmission foci differ in their susceptibility to snail population control measures. Spatially explicit dispersal models that consider the spatial and temporal patterns of connectivity between aquatic habitats will contribute to improved snail surveillance and more focused control for urinary schistosomiasis at a local level.

A geographic information and remote sensing based model for prediction of Oncomelania hupensis habitats in the Poyang Lake area, China

A model was developed using remote sensing and geographic information system technologies for habitat identification of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum, in the Poyang Lake area, China. In a first step, two multi-temporal Landsat TM 5 satellite images, one from the wet and the second from the dry season, were visually classified into different land-use types. Next, the normalized difference vegetation index was extracted from the images and the tasseled-cap transformation was employed to derive the wetness feature. Our model predicted an estimated 709 km2 of the marshlands in Poyang Lake as potential habitats for O. hupensis. Near-ground temperature measurements in April and August yielded a range of 22.8-24.2 degrees C, and pH values of 6.0-8.5 were derived from existing records. Both climatic features represent suitable breeding conditions for the snails. Preliminary validation of the model at 10 sites around Poyang Lake revealed an excellent accuracy for predicting the presence of O. hupensis. We used the predicted snail habitats as centroids and established buffer zones around them. Villages with an overall prevalence of S. japonicum below 3% were located more than 1200m away from the centroids. Furthermore, a gradient of high-to-low prevalence was observed with increasing distance from the centroids. In conclusion, the model holds promise for identifying high risk areas of schistosomiasis japonica and may become an important tool for the ongoing national schistosomiasis control programme. The model is of particular relevance for schistosome-affected regions that lack accurate surveillance capabilities and are large enough to be detected at most commercially available remote sensing scales.

A review of geographic information system and remote sensing with applications to the epidemiology and control of schistosomiasis in China

Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere.