Fig 1.
Study framework for enhancing early identification of schistosomiasis hotspots (early identification refers to the use of infection data only from the first year to develop prediction models).
Fig 2.
Map of hotspot areas around Lake Victoria, highlighted in red points, in Tanzania (top) and Kenya (bottom) using persistent hotspot definitions I–II.
The map layers were created using publicly available world map data from Natural Earth, accessed via the R package rnaturalearth [24].
Table 1.
Average relative improvements (ARIs, %) for each model in prediction accuracy on test sets from the proposed spatially weighted method using seven different predictor configurations C2–C8, compared to the method using the baseline infection data only (C1).
Table 2.
The relative improvements (RIs, %) for each scenario obtained using the spatially weighted data fusion method with different predictor configurations (C2–C8), compared to the method with configuration C1.
Fig 3.
Under PHS definition I, the RIs, in prediction accuracy on unprocessed test sets, obtained from the models trained using pre-processed synthetic data based on the proposed different predictor configurations, compared to models using unprocessed original imbalanced training data, where the best model with the highest prediction accuracy was considered for each method (i.e., y-axis).