Anticipate

ANTICIPATE / RESEARCH

Earlier detection.
Better hospital preparedness.

ANTICIPATE combines artificial intelligence, epidemic modelling and genome-based diagnostics to help hospitals detect emerging respiratory pathogens and prepare for their impact on patients, staff and essential care.

01 / OBJECTIVES

Four connected research objectives

01

Hospital epidemic modelling

Construct a realistic individual-based model that connects hospital venues, the roles of patients and staff, and transmission through contaminated objects.

02

AI decision support

Develop reinforcement-learning methods that identify mitigation policies according to epidemiological, operational, biostatistical and health-economic criteria.

03

Pandemic data intelligence

Study data collected during COVID-19 and use simulation to determine which epidemiological markers are most informative for hospital decisions.

04

Genome-based surveillance

Develop and evaluate a large-scale symptom-driven sequencing platform for the early detection and monitoring of novel viral pathogens.

02 / EXPECTED OUTCOMES

Tools and evidence for hospital preparedness

The project aims to deliver:

  1. A realistic individual-based model of virus transmission in hospitals.
  2. An AI-driven expert system for learning optimal mitigation policies.
  3. Evidence-based advice for pandemic and seasonal influenza mitigation.
  4. A symptom-driven screening platform for early pathogen detection.
  5. A catalogue assessing the value of hospital data collected during COVID-19.
  6. Models for evaluating data-collection strategies in future pandemics.