Publications by authors named "Fe Esperanza J Espino"
Article Synopsis
- * Using a sample of over 9,000 individuals, the results found that a Random Forest model using specific serological markers outperformed traditional methods in predicting recent infections in an area of varying transmission intensity.
- * Findings indicate that serological markers are effective in evaluating malaria transmission status in regions approaching elimination, demonstrating the potential of machine learning to enhance monitoring efforts.
Following substantial progress in malaria control in the Philippines, new surveillance approaches are needed to identify and target residual malaria transmission. This study evaluated an enhanced surveillance approach using rolling cross-sectional surveys of all health facility attendees augmented with molecular diagnostics and geolocation. Facility surveys were carried out in three sites representing different transmission intensities: Morong, Bataan (pre-elimination), Abra de Ilog, Occidental Mindoro (stable medium risk), and Rizal, Palawan (high risk, control).
View Article and Find Full Text PDF