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

Abstract

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.

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

“…Ecological niche modeling has been widely employed in infectious disease research [ 16 ] and it is useful for the analysis of associations among environmental risk factors and infection prevalence as a means by which to predict disease transmission [ 17 , 18 ]. To date, ecological niche modeling has been used to predict the potential habitats of Oncomelania hupensis (the intermediate host of S. japonicum ) [ 19 , 20 , 21 ] and to predict schistosomiasis vectorial capacity based on future climate scenarios [ 22 ]. Previous studies have successfully used ecological niche modeling for the prediction of schistosomiasis transmission, but few of these studies have assessed the direct risk of schistosomiasis transmission by ecological niche modeling.…”

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

“…In this study, NDWI was positively associated with infection risk, with some evidence of a threshold effect. This suggests that residents in homes situated in areas with more surface water nearby (within 1 km) have a greater risk of S. japonicum infection -an association that could be due to increased opportunities for human exposure to schistosomes through water contact, as has been previously found (9,19,49,(51)(52)(53)(54). In a similar vein, we found that homes that were closer to waterways, as well as those at lower elevations were more likely to have S. japonicum infection than those that were nearer to waterways or situated at higher elevations.…”

“…There is considerable variability in key predictors of snail habitat when comparing across different ecosystem types, even within regions of China. For example, one study spanning several hundred kilometers along the Yangtze River in Anhui Province indicated that distance to the nearest river was the most important predictor of snail habitats in marshland ecosystems, whereas 100-m resolution summaries of mean temperature and annual precipitation were the most important predictors within the hilly regions of the Province (19). Notably, across smaller geographic areas, factors like ecosystem type and climatic conditions would not be expected to vary meaningfully, highlighting the ongoing need to identify metrics that can predict schistosomiasis at ne spatial scales.…”

Three Gorges Dam: The changing trend of the density of Oncomelania hupensis, the intermediate host for Schistosoma japonicum, in reaches of the Yangtze River, 1990-2019

“…The abundance of snails can promote schistosomiasis transmission, so understanding the dynamics and spatial heterogeneity of snail populations are critical for schistosomiasis risk assessment and control [21][22][23]. Controlling the snail density at a low level is essential for blocking and eliminating schistosomiasis [24], however, most previous studies on the distribution of snail focused on the suitability probability, and rarely quanti ed the snail population density [25][26][27]. Also, there is a knowledge gap to systematically clarify the long-term and continuous dynamics of snail density both upstream and downstream TGD following major environmental change [28][29].…”