Design of an Integrated Model for Deep Causal Interpretation and Evolutionary Prediction of Zika Virus Mutations

Identifying genetic changes that elevate Zika Virus (ZIKV) virulence is vital for epidemic forecasting and vaccine development Traditional phylogenetic and regression methods map variation but seldom pinpoint mutations driving phenotypic change. We present an integrated deep-learning and simulation framework that tracks ZIKV's sequence-to-consequence trajectory. A Spatio-Temporal Generative Adversarial Network (ST-GANet) learns region-time-mutation patterns to reveal evolutionary hotspots. A Causal Mutation Gradient Mapper (CMGM) then estimates each mutation's directional influence on virulence. A Viral-Host Interaction Transformer (VHIT) predicts how prioritized mutations alter Envelope and NS1 protein -receptor binding. Using transcriptomes from infected human brain progenitor cells, a Pathogenicity Potential Simulation Engine (PPSE) models resulting intracellular signalling disruptions. An Evolutionary Route Planner (ERP) identifies fitness-maximizing mutational paths under immune pressure. Together, these modules reveal how subtle sequence changes can reshape epidemiological risk and support real-time flavivirus molecular surveillance.