Projecting the proliferation risk of Oncomelania hupensis in China driven by SSPs: A multi-scenario comparison and integrated modeling study

Gong, Yanfeng; Hu, Xiaokang; Hao, Yufan; Luo, Zhitian; Feng, Jiaxin; Xue, Jing-Bo; Guo, Zhaoyu; Li, Yinlong; Zhang, Lijuan; Shang, Xianwen; Lv, Shan; Xu, Jing; Li, Shi-Zhu

doi:10.1016/j.accre.2022.02.004

Cited by 13 publications

(11 citation statements)

References 37 publications

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“…Texture indicators, particularly B6 and B7, have proven to be highly in uential, surpassing the importance of previously recognized variables such as NL, LST, NDVI, and so on. These ndings are supported by those reported in recent studies [28][29][30].…”

Section: Discussionsupporting

confidence: 92%

Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Zhang,

Lv,

Dai

et al. 2024

Preprint

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Background Schistosomiasis japonica poses a significant health issue in China, largely due to the spatial distribution of Oncomelania hupensis, the only intermediate host of Schistosoma, which directly affects schistosomiasis incidence. This study therefore aimed to address the limitations in existing remote sensing studies, particularly the oversight of spatial scale and seasonal variations in snail habitats by introducing a multi-source data-driven Random Forest approach. Methods This method effectively integrates bottomland and ground-surface texture data with traditional environmental variables for a more comprehensive and accurate snail habitat analysis. Four distinct models focusing on lakes and marshlands in Guichi, China, were developed: the baseline model, including ground-surface texture, bottomland variables, and environmental variables; Model 1, including only environmental variables; Model 2, including ground-surface texture and environmental variables; and Model 3, including bottomland and environmental variables. Results The baseline model outperformed the others, achieving a true skill statistic of 0.93, accuracy of 0.97, kappa statistic of 0.94, and area under the curve of 0.98. The findings identified key high-risk snail habitats, particularly along major rivers and lakes in a belt-like distribution, particularly near the Yangtze River, Qiu Pu River, and surrounding areas of Shengjin Lake, Jiuhua River, and Qingtong River. Conclusions This study providing vital data for effective snail monitoring, control strategies, and schistosomiasis prevention. This approach may also be applicable in locating other epidemic hosts with similar survival and ecological characteristics.

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Section: Discussionsupporting

confidence: 92%

Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Zhang,

Lv,

Dai

et al. 2024

Preprint

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“…In this study, using climatic and environmental variables, five machine learning algorithm models GBM, C5.0, XGB, RF, and SVM algorithms, predicted the potential distribution of O. hupensis in Suzhou City accurately, which may be used to guide and optimize O. hupensis surveys. The climatic and ecological variables, including temperature and precipitation, were usually considered to be the most important impact factors for the snail distribution and they play a decisive role in many big-scale studies (Gong et al, 2022). However, considering the difference in the microenvironment and it may affect the survival of the snail in a fine-scale study (Liu M.-M. et al, 2021), more environmental variables were picked up into machine learning algorithm models, including the silt content in soil, clay content in soil, population density, and night-time lights (Zheng et al, 2014;Gao et al, 2015), and they show a significant contribution role for the O. hupensis distribution prediction in this study.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Distribution Pattern of Oncomelania hupensis Caused by Multiple Environmental Factors Using Ecological Niche Models

Shi

Gong

Zhao

et al. 2022

Front. Environ. Sci.

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Objective: This study aimed to predict the spatial and temporal distribution pattern of Oncomelania hupensis (O. hupensis) on a fine scale based on ecological niche models, so as to provide insights into O. hupensis surveillance.Methods: Geographic distribution and environmental variables of O. hupensis in Suzhou City were collected from 2016 to 2020. Five machine learning algorithms were used, including eXtreme gradient boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosted (GBM), and C5.0 algorithms, to predict the distribution of O. hupensis and investigate the relative contribution of each environmental variable. The accuracy of the five ecological niche models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with ten-fold cross-validation.Results: Five models predicted that the potential distribution of O. hupensis was in southwestern areas of Wuzhong, Wujiang, Taichang, and Xiangcheng counties. The AUC of RF, XGB, GBM, SVM, and C5.0 algorithms were 0.8233, 0.8051, 0.7938, 0.7897, and 0.7282, respectively. Comparing the predictive results and the truth of O. hupensis distribution in 2021, XGB and GBM models were shown to be more effective. The six greatest contributors to predicting potential O. hupensis distribution included silt content (13.13%), clay content (10.21%), population density (8.16%), annual accumulated temperatures of ≥0°C (8.12%), night-time lights (7.67%), and average annual precipitation (7.23%).Conclusions: Environmental factors play a key role in the spatial and temporal distribution pattern of O. hupensis. The XGB and GBM machine learning algorithms are effective and highly accurate for fine-scale prediction of potential O. hupensis distribution, which provides insights into the surveillance of O. hupensis.

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“…The epidemics of NTDs are sensitive in different ways to environmental and socioeconomic conditions [ 27 , 28 , 29 ], so the risk evaluation for NTDs requires data from multiple sources and multiple aspects, such as the geographical distributions of the pathogens, vectors, or host populations, as well as their related environmental determinants [ 30 , 31 ]. Ecological niche models integrate these datasets and utilize statistical approaches for predicting the potential distribution of vector species from survey-based observations [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

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

Xue

Hao

et al. 2022

TropicalMed

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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.

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Projecting the proliferation risk of Oncomelania hupensis in China driven by SSPs: A multi-scenario comparison and integrated modeling study

Cited by 13 publications

References 37 publications

Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Spatial and Temporal Distribution Pattern of Oncomelania hupensis Caused by Multiple Environmental Factors Using Ecological Niche Models

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

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