Host-Parasite Interactions and Phase Transition in Strongyloidiasis: A Digital Twin Approach

Abstract

Strongyloidiasis is a persistent helminth infection capable of maintaining itself within the human host for decades through an autoinfection cycle. Unlike most parasitic infections, strongyloidiasis may remain clinically silent for years before transitioning abruptly into hyperinfection or disseminated disease, particularly under conditions of immunosuppression. Mortality in these advanced stages is high, yet risk stratification and early warning systems are lacking. A digital twin framework for strongyloidiasis, defined as a continuously updated virtual representation integrating parasite replication dynamics, host immune competence, and clinical context, offers a novel approach to predictive modelling of disease stability and collapse. By incorporating longitudinal immune markers, exposure history, treatment data, and immunosuppressive triggers, such a model could forecast tipping points, identify individuals at imminent risk, and support pre-emptive intervention. Here, we describe the conceptual framework, biological architecture, and translational relevance of strongyloidiasis digital twins, highlighting their potential to prevent catastrophic outcomes in an often-overlooked infection.