POST-INFECT AI TWIN AI-powered prediction, diagnosis support and personalised management of post-infection long-term conditions

Project title POST-INFECT AI TWIN - AI-powered prediction, diagnosis support and personalised management of post-infection long-term conditions

Problem addressed

Post-infection long-term conditions are heterogeneous, underdiagnosed and inconsistently managed across Europe. The lack of early risk stratification, validated biomarkers and integrated care pathways results in delayed diagnosis, fragmented interventions, persistent disability and significant long-term costs for healthcare systems.

Project vision

POST-INFECT AI TWIN aims to develop and validate an AI-enabled, interoperable decision-support ecosystem, grounded in the Virtual Human Twin (VHT) paradigm, to predict risk, support early diagnosis and optimise personalised management of post-infection conditions across diverse populations.

 Core objectives

·       Identify risk and protective factors for post-infection conditions by integrating clinical, biological, functional and patient-reported data;

·       Develop explainable AI models for early diagnosis, disease stratification and progression monitoring;

·       Enable personalised therapeutic and rehabilitation pathways (physical, cognitive and psychological);

·       Improve coordination between primary and specialised care and generate actionable evidence for public health authorities and policymakers.

Innovative elements

·       Multimodal AI models combining longitudinal clinical data, biomarkers and PROMs;

·       Virtual Human Twin–powered computational representations to simulate disease trajectories, risk evolution and response to interventions;

·       FAIR-by-design data pipelines aligned with EHDS and EOSC principles;

·       Built-in bias monitoring and mitigation addressing sex/gender, age and socio-economic disparities. 

Expected impact

·       Improved patient recovery, quality of life and mental health outcomes;

·       More efficient allocation of healthcare resources and reduced long-term economic burden;

·       Evidence-based inputs for clinical guidelines, training of healthcare professionals and strengthened public health preparedness;

Role of Acube (SME – AI & Digital Health)

Acube will act as core Technology and AI partner (not coordinator), responsible for:

·       Design and development of AI/ML pipelines and decision-support algorithms;

·       Integration of VHT-inspired models into clinical decision support tools;

·       Data engineering, interoperability (HL7-FHIR) and federated learning approaches;

·       Development of clinician-facing dashboards and tools for healthcare authorities;

·       AI governance, ethics-by-design and regulatory readiness, including interaction support with European Medicines Agency.

In addition, Acube can involve an organisation with proven expertise in Virtual Human Twin methodologies and infrastructures, able to support the achievement of the project objectives.

Important note

Acube cannot act as project coordinator, but is available to take a substantial technical role as AI/VHT partner, in close collaboration with the clinical coordinator.

Suggested Principal Investigator / Coordinator profile

The project requires a clinical-academic coordinator with strong leadership in post-infectious disease research, ideally:

·       A university hospital or medical faculty with experience in Horizon Europe Health RIAs;

·       Proven expertise in infectious diseases, immunology or internal medicine (e.g. long COVID, post-bacterial syndromes);

·       Direct access to patient cohorts, clinical studies and biobanks. 

Partners sought (indicative consortium composition)

·       Clinical partners: university hospitals, infectious disease units, rehabilitation and long-term care centres;

·       Biomedical research organisations: immunology, inflammation, biomarker discovery and translational medicine;

·       Virtual Human Twin and modelling organisations: computational modelling, simulation and digital twin infrastructures;

·       Patient organisations: co-design, patient engagement and validation of outcomes;

·       Public health bodies: national or regional authorities supporting guideline uptake and policy integration;

·       SSH partners: behavioural sciences, health inequalities, ethics and societal impact;

·       Health economics and HTA experts: evaluation of clinical, economic and social implications.

Cross-cutting dimensions

• Integration of Social Sciences and Humanities (SSH);

• Explicit focus on sex/gender, vulnerable populations and equitable access to care;

• Alignment with European Health Data Space and European Open Science Cloud.

  Acube_Technology & AI Partner_

  e-mail: fabrizio.coccetti@acube.ai