Personalised digital twin models for Trypanosoma cruzi : improving prognosis, therapy, and long-term complications

Chagas disease, caused by Trypanosoma cruzi, remains a major public-health challenge across Latin America and increasingly in non-endemic regions. Although most infected individuals enter a long indeterminate phase, a substantial minority progress to chronic cardiomyopathy or gastrointestinal complications, often after years of silent damage. Predicting which patients will deteriorate remains one of the most difficult tasks in Chagas care, as disease trajectories are shaped by complex interactions between parasite persistence, immune regulation, tissue remodelling, comorbidities, and treatment response. Digital twin technology is an adaptive computational model that continuously integrates biological, clinical, and behavioural data, offering a new approach for predicting risk and management. By assimilating parasite kinetics, immune activity, cardiac electrical and structural changes, pharmacological exposure, and patient-specific factors, a T. cruzi digital twin could forecast clinical progression, monitor treatment efficacy, and support personalised therapeutic decisions long before irreversible pathology evolves. Here, we discuss the basis for a digital twin approach in Chagas disease, exploring its potential applications in clinical and public-health settings, and highlight the ethical, technical, and translational challenges to make this technology feasible in endemic and resource-limited environments.