Identification of high-risk habitats of Oncomelania hupensis, the intermediate host of schistosoma japonium in the Poyang Lake region, China: A spatial and ecological analysis

Abstract

Background Identifying and eliminating snail habitats is the key measure for schistosomiasis control, critical for the nationwide strategy of eliminating schistosomiasis in China. Here, our aim was to construct a new analytical framework to predict high-risk snail habitats based on a large sample field survey for Oncomelania hupensis , providing guidance for schistosomiasis control and prevention. Methodology/Principal findings Ten ecological models were constructed based on the occurrence data of Oncomelania hupensis and a range of variables in the Poyang Lake region of China, including four presence-only models (Maximum Entropy Models, Genetic Algorithm for rule-set Production, Bioclim and Domain) and six presence-absence models (Generalized Linear Models, Multivariate Adaptive Regression Splines, Flexible Discriminant Analysis, as well as machine algorithmic models–Random Forest, Classification Tree Analysis, Generalized Boosted Model), to predict high-risk snail habitats. Based on overall predictive performance, we found Presence-absence models outperformed the presence-only models and the models based on machine learning algorithms of classification trees showed the highest accuracy. The highest risk was located in the watershed of the River Fu in Yugan County, as well as the watershed of the River Gan and the River Xiu in Xingzi County, covering an area of 52.3 km 2 . The other high-risk areas for both snail habitats and schistosomiasis were mainly concentrated at the confluence of Poyang Lake and its five main tributaries. Conclusions/Significance This study developed a new distribution map of snail habitats in the Poyang Lake region, and demonstrated the critical role of ecological models in risk assessment to directing local field investigation of Oncomelania hupensis . Moreover, this study could also contribute to the development of effective strategies to prevent further spread of schistosomiasis from endemic areas to non-endemic areas.

Assessment of vector-host-pathogen relationships using data mining and machine learning

“…In jackknife cross-validation, 80.03%, 80.90% and 79.16% respectively. accuracy; sensitivity and specificity, 10-fold cross-validation Relevant features 31,206,514 [43] Use of ML to identify high risk snail habitats as function of Schistosoma japonium infection control and elimination Xia C, et al 2019 Prediction RF, CTA, GB Q1, Q2, Q3, Q7, Q8 Population, Other SB level RF Model (AUC = 0.96), ensemble model (AUC = 0.89, sensitivity − 0.79 - specificity = 0.82). 10 Fold Cross-Validation climatic, environment and economic factors (very low, low, moderate, high and very high) 29,738,521 [29] Random forest classifier to identify Salmonella enterica strains associated with extraintestinal disease using measured burden of atypical mutations in protein coding genes across independently evolved lineages.…”

“…The model accuracy range was 82–97%. The problems addressed included: the identification of high risk snail habitats as a function of Schistosoma japonicum infection [43] , modelling of tick bite risk based on ecological factors [44] , predicting the global distribution of Aedes mosquitoes and the effects of seasonal changes on their range [45] , [46] and the prediction of Dengue virus outbreak risk based on climate [47] , [48] .…”

Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images

“…Interestingly, these areas were identified as high-risk transmission areas in the present study. In areas characterized by vast marshlands, multiple grasslands, dense vegetation, difficulty in managing water levels and extensive snail distribution where schistosomiasis was once hyper-endemic and transmission had been controlled or interrupted [ 9 ], S. japonicum transmission potential remains due to the distribution of O. hupensis snails following flooding. Any relaxing of control interventions would therefore likely result in re-emergence of schistosomiasis infection.…”

“…Regular seasonal fluctuations in the water level of Poyang Lake create favorable conditions for snail breeding in the marshlands around this lake. Flooding in the area in 2017 contributed to the expansion of snail habitats along Poyang Lake [ 5 , 9 , 10 ] by 490 thousand km 2 , accounting for 98% of newly detected and re-emerging snail habitats in Jiangxi Province [ 11 ]. Additionally, infected bovines maintain a reservoir of schistosomiasis infection in the Poyang Lake region, acting as a source of onward transmission [ 12 ].…”

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

“…Thus the risk of misclassification is high, in particular as the resolution of the satellite optical sensors is generally not sufficient for unequivocal identification of a snail habitat [16]. To improve identification, we therefore attempted a modelling strategy with machine-learning as the approach to achieve superior accuracy on the basis of our original research [17].…”