The Potential Distribution Prediction of Oncomelania hupensis Based on Newly Emerging and Reemergent Habitats - China, 2015-2019

Interactive and lag effects of environmental factors on the density of schistosome-transmitting Oncomelania hupensis: A twelve-year monthly repeated survey

Schistosomiasis is a significant public health threat, and Oncomelania hupensis is the only intermediate host for schistosoma japonicum. We conducted 12-year monthly repeated surveys to explore the interactive and lag effects of environmental factors on snail density and to monitor their long-term and seasonal trends in a bottomland around the Dongting Lake region in China. Relevant environmental data were obtained from multiple sources. A Bayesian kernel machine regression model and a Bayesian temporal model combined with a distributed lag model were constructed to analyze interactive and lag effects of environmental factors on snail density. The results indicated the average annual snail density in the study site exhibited an increasing and then decreasing trend, peaking in 2013. Snail densities were the highest in October and the lowest in January in a year. Normalized Difference Vegetation Index (NDVI) and water level were the most effective predictors of snail density, with potential interactions among temperature, precipitation, and NDVI. The mean minimum temperature in January, water level, precipitation and NDVI were positively correlated with snail density at lags ranging from 1 to 4 months. These findings could serve as references for relevant authorities to monitor the changing trend of snail density and implement control measures, thereby reducing the occurrence of schistosomiasis.

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: Potential differential drivers and trend in the spatio-temporal evolution of the change in snail density based on a Bayesian spatial-temporal model and 5-year longitudinal study

BACKGROUND

Snail abundance varies spatially and temporally. Few studies have elucidated the different effects of the determinants affecting snail density between upstream and downstream areas of the Three Gorges Dam (TGD). We therefore investigated the differential drivers of changes in snail density in these areas, as well as the spatial-temporal effects of these changes.

METHODS

A snail survey was conducted at 200 sites over a 5-year period to monitor dynamic changes in snail abundance within the Yangtze River basin. Data on corresponding variables that might affect snail abundance, such as meteorology, vegetation, terrain and economy, were collected from multiple data sources. A Bayesian spatial-temporal modeling framework was constructed to explore the differential determinants driving the change in snail density and the spatial-temporal effects of the change.

RESULTS

Volatility in snail density was unambiguously detected in the downstream area of the TGD, while a small increment in volatility was detected in the upstream area. Regarding the downstream area of the TGD, snail density was positively associated with the average minimum temperature in January of the same year, the annual Normalized Difference Vegetation Index (NDVI) of the previous year and the second, third and fourth quartile, respectively, of average annual relative humidity of the previous year. Snail density was negatively associated with the average maximum temperature in July of the previous year and annual nighttime light of the previous year. An approximately inverted "U" curve of relative risk was detected among sites with a greater average annual ground surface temperature in the previous year. Regarding the upstream area, snail density was positively associated with NDVI and with the second, third and fourth quartile, respectively, of total precipitation of the previous year. Snail density was negatively associated with slope.

CONCLUSIONS

This study demonstrated a rebound in snail density between 2015 and 2019. In particular, temperature, humidity, vegetation and human activity were the main drivers affecting snail abundance in the downstream area of the TGD, while precipitation, slope and vegetation were the main drivers affecting snail abundance in the upstream area. These findings can assist authorities to develop and perform more precise strategies for surveys and control of snail populations.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination

BACKGROUND

Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence.

METHODS

In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data.

RESULTS

The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen's kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (an indicator of surface water presence) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways.

CONCLUSION

Our results suggest that in low-transmission environments, leveraging open-source environmental data can yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Effect of Health Education on Schistosomiasis Control Knowledge, Attitude, and Practice after Schistosomiasis Blocking: Results of a Longitudinal Observational Study in the Field

OBJECTIVES

Schistosomiasis is a zoonotic infectious disease that seriously harms people's physical and mental health. As early as 1985, the WHO suggested that health education and health promotion should be the focus of schistosomiasis prevention work. This study aimed to explore the effect of health education on controlling the risk of schistosomiasis transmission after schistosomiasis blocking and to provide a scientific basis for the further improvement of intervention strategies after schistosomiasis interruption in China and other endemic countries.

METHODS

In Jiangling County, Hubei Province, China, one severe, one moderate, and one mildly endemic village were selected as the intervention group; two severe, two moderate, and two mildly endemic villages were selected as the control group. In towns with different epidemic types, a primary school was randomly selected for intervention. In September 2020, a baseline survey was carried out through a questionnaire survey to understand the knowledge, attitudes, and practices (KAP) of adults and students concerning schistosomiasis control. Next, two rounds of health education interventions for schistosomiasis control were carried out. The evaluation survey was conducted in September 2021 and the follow-up survey conducted in September 2022.

RESULTS

Compared with the baseline survey, the qualified rate of the KAP on schistosomiasis prevention of the control group in the follow-up survey increased from 79.1% (584/738) to 81.0% (493/609) (p > 0.05); in the intervention group, the qualified rate of the KAP on schistosomiasis control increased from 74.9% (286/382) to 88.1% (260/295) (p < 0.001). In the baseline survey, the qualified rate of the KAP of the intervention group was lower than that of the control group, and in the follow-up survey, the qualified rate of the KAP of the intervention group was 7.2% higher than that of the control group (p < 0.05). Compared with the baseline survey, the accuracy rates of the KAP of the intervention group's adults were higher than those of the control group, with statistical significance (p < 0.001). Compared with the baseline survey, the qualified rate of the students' KAP in the follow-up survey increased from 83.8% (253/302) to 97.8% (304/311) (p < 0.001). In the follow-up survey, the accuracy rate of the knowledge, attitudes, and practices of the students was significantly different from the baseline accuracy (p < 0.001).

CONCLUSION

a health education-led risk control model of schistosomiasis can significantly improve schistosomiasis control knowledge among adults and students, establishing correct attitudes and leading to the development of correct hygiene habits.

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.

Oncomelania hupensis Distribution and Schistosomiasis Transmission Risk in Different Environments under Field Conditions

The goal of schistosomiasis prevention and control in China is shifting from transmission interruption to elimination. However, the area inhabited by the intermediate host, the snail Oncomelania hupensis, has not changed much in recent years. Different environmental types have different impacts on snail breeding, and understanding these differences is conducive to improving the efficiency of snail monitoring and control and to saving resources. Based on previous epidemiological data, we selected 199 villages in 2020 and 269 villages in 2021 from transmission control, transmission interruption, and elimination areas of snail breeding. Snail surveys were conducted in selected villages using systematic sampling and/or environmental sampling methods in six types of snail-breeding environments (canals, ponds, paddy fields, dry lands, bottomlands, and undefined environments). All live snails collected from the field were evaluated for Schistosoma japonicum infection using the microscopic dissection method, and a subsample of snails was subjected to loop-mediated isothermal amplification (LAMP) to assess the presence of S. japonicum infection. Snail distribution data and infection rate and nucleic acid positive rate of schistosomes in snails were calculated and analyzed. The 2-year survey covered 29,493 ha of the environment, in which 12,313 ha of snail habitats were detected. In total, 51.16 ha of new snail habitats and 107.76 ha of re-emergent snail habitats were identified during the survey. The occurrence rate of snails in canals (10.04%, 95% CI: 9.88-10.20%) and undefined environments (20.66%, 95% CI: 19.64-21.67%) was relatively high in 2020, and the density of snails in bottomlands (0.39, 95% CI: 0.28-0.50) and undefined environments (0.43, 95% CI: 0.14-1.60) was relatively high in 2021. Of the 227,355 live snails collected in this study, none were S. japonicum-positive as determined by microscopy. Of the 20,131 pooled samples, however, 5 were S. japonicum-positive based on LAMP analysis, and they were distributed in three environmental types: 3 in bottomland, 1 in dry land, and 1 in a canal. The bottomland environment has a high risk of schistosomiasis transmission because it contains a large area of newly emerging and re-emerging snail habitats, and it also had the most breeding snails infected with S. japonicum. Thus, this habitat type should be the key target for snail monitoring and early warning and for the prevention and control of schistosomiasis.

Open-source environmental data as an alternative to snail surveys to assess schistosomiasis risk in areas approaching elimination

Background: Although the presence of intermediate snails is a necessary condition for local schistosomiasis transmission to occur, using them as surveillance targets in areas approaching elimination is challenging because the patchy and dynamic quality of snail host habitats makes collecting and testing snails labor-intensive. Meanwhile, geospatial analyses that rely on remotely sensed data are becoming popular tools for identifying environmental conditions that contribute to pathogen emergence and persistence. Methods: In this study, we assessed whether open-source environmental data can be used to predict the presence of human Schistosoma japonicum infections among households with a similar or improved degree of accuracy compared to prediction models developed using data from comprehensive snail surveys. To do this, we used infection data collected from rural communities in Southwestern China in 2016 to develop and compare the predictive performance of two Random Forest machine learning models: one built using snail survey data, and one using open-source environmental data. Results: The environmental data models outperformed the snail data models in predicting household S. japonicum infection with an estimated accuracy and Cohen’s kappa value of 0.89 and 0.49, respectively, in the environmental model, compared to an accuracy and kappa of 0.86 and 0.37 for the snail model. The Normalized Difference in Water Index (NDWI) within half to one kilometer of the home and the distance from the home to the nearest road were among the top performing predictors in our final model. Homes were more likely to have infected residents if they were further from roads, or nearer to waterways. Conclusion: Our results suggest that in low-transmission environments, investing in training geographic information systems professionals to leverage open-source environmental data could yield more accurate identification of pockets of human infection than using snail surveys. Furthermore, the variable importance measures from our models point to aspects of the local environment that may indicate increased risk of schistosomiasis. For example, households were more likely to have infected residents if they were further from roads or were surrounded by more surface water, highlighting areas to target in future surveillance and control efforts.

Transmission Risk Predicting for Schistosomiasis in Mainland China by Exploring Ensemble Ecological Niche Modeling

Schistosomiasis caused by Schistosoma japonicum is one of the major neglected tropical diseases worldwide. The snail Oncomelania hupensis is the only intermediate host of S. japonicum, which is recognized as an indicator of the schistosomias occurrence. In order to evaluate the risk of schistosomiasis in China, this work investigate the potential geographical distribution of host snail habitus by developing an ensemble ecological niche model with reference to the suitable environmental factors. The historical records of snail habitus were collected form the national schistosomiasis surveillance program from the year of 2005 to 2014. A total of 25 environmental factors in terms of the climate, geographic, and socioeconomic determinants of snail habitats were collected and geographically coded with reference to the snail data. Based on the correlations among snail habitats and the geographically associated environmental factors, an ensemble ecological niche model was developed by integrating ten standard models, aiming for improving the predictive accuracy. Three indexes are used for model performance evaluation, including receiver operating characteristic curves, kappa statistics, and true skill statistics. The model was used for mapping the risk of schistosomiasis in the middle and lower reaches of the Yangtze River. The results have shown that the predicted risk areas were classified into low risk (4.55%), medium risk (2.01%), and high risk areas (4.40%), accounting for 10.96% of the land area of China. This study demonstrated that the developed ensemble ecological niche models was an effective tool for evaluating the risk of schistosomiasis, particularly for the endemic regions, which were not covered by the national schistosomiasis control program.

Cross-watershed distribution pattern challenging the elimination of Oncomelania hupensis, the intermediate host of Schistosoma japonica, in Sichuan province, China

Background

Snail control is critical to schistosomiasis control efforts in China. However, re-emergence of Oncomelania hupensis is challenging the achievements of schistosomiasis control. The present study aimed to test whether the amphibious snails can spread across watersheds using a combination of population genetics and geographic statistics.

Methods

The digital maps and attributes of snail habitats were obtained from the national survey on O. hupensis. Snail sampling was performed in 45 counties of Sichuan Province. The cox1 gene of specimens was characterized by sequencing. Unique haplotypes were found for phylogenetic inference and mapped in a geographical information system (GIS). Barriers of gene flow were identified by Monmonier’s maximum difference algorithm. The watercourses and watersheds in the study area were determined based on a digital elevation model (DEM). Plain areas were defined by a threshold of slope. The slope of snail habitats was characterized and the nearest distance to watercourses was calculated using a GIS platform. Spatial dynamics of high-density distributions were observed by density analysis of snail habitats.

Results

A total of 422 cox1 sequences of O. hupensis specimens from 45 sampling sites were obtained and collapsed into 128 unique haplotypes or 10 clades. Higher haplotype diversity in the north of the study area was observed. Four barriers to gene flow, leading to five sub-regions, were found across the study area. Four sub-regions ran across major watersheds, while high-density distributions were confined within watersheds. The result indicated that snails were able to disperse across low-density areas. A total of 63.48% habitats or 43.29% accumulated infested areas were distributed in the plain areas where the overall slope was < 0.94°. Approximately 90% of snail habitats were closer to smaller watercourses. Historically, high-density areas were mainly located in the plains, but now more were distributed in hilly region.

Conclusions

Our study showed the cross-watershed distribution of Oncomelania snails at a large scale. Natural cross-watershed spread in plains and long-distance dispersal by humans and animals might be the main driver of the observed patterns. We recommend cross-watershed joint control strategies for snail and schistosomiasis control.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05496-0.