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

Abstract

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.

Keywords: China; Geographic information systems; Infectious disease surveillance; Machine learning; Oncomelania hupensis; Prevention and control; Remote sensing technology; Schistosomiasis; Snails.

Fig. 1

Depiction of household inclusion and exclusion

Fig. 2

Maps of the study villages

Fig. 3

Receiver Operating Characteristics (ROC) Area Under the Curve (AUC) for snail and environmental models

Fig. 4

Variable importance plots for the snail and environmental data models. For each of the three models generated with the snail data and the environmental data, variable importance was determined using Mean Decrease in Accuracy (MDA). Each variable is assigned one color across all three models such that color can be used to highlight major shifts in variable importance ranks between models

Fig. 5

Prediction map showing the probability of S. japonicum infection using the top-performing environmental data model. The final top performing model was defined as the one with the highest kappa, accuracy, and receiver operating characteristic (ROC) area under the curve (AUC), respectively. Model performance metrics (Cohen’s kappa and accuracy) highlighted that the open-source environmental data models outperformed the snail data models. The top performing environmental data model was used to create a prediction surface of the probability of S. japonicum infection across the entire study area