Prashanth Selvaraj

This study characterizes a new Anopheles funestus colony (FUTAZ) established in Tanzania, comparing its fitness traits and genetic markers to wild and existing colonies.

This paper develops a multitask deep learning emulator to calibrate an agent-based malaria transmission model using simulations and real-world data.

This study improves mosquito age prediction from mid-infrared spectra using transfer learning and dimensionality reduction, achieving high accuracy across different populations.

The paper develops a gene drive effector that retards Plasmodium development in transgenic Anopheles gambiae mosquitoes via midgut antimicrobial peptides, predicted to eliminate malaria across transmission scenarios.

This study models the effectiveness of population replacement gene drive mosquitoes in eliminating malaria under different transmission settings and finds that combining them with traditional controls can achieve local elimination in low to moderate transmission areas.

This paper develops an agent-based model to study insecticide resistance and gene drive impacts on malaria transmission in Anopheline populations.