Data Integration

GeoAI4EI integrates multi-source data to capture the full context in which diseases emerge and spread. No single data source tells the whole story — combining complementary perspectives is what makes the toolbox useful for epidemic intelligence.

The six data domains

Domain	Examples	Why it matters
🦠 Epidemiological	case notifications, laboratory results, syndromic surveillance	The direct signal of disease occurrence and spread
🚌 Mobility	transport flows, travel patterns, commuting	How pathogens and people move across space
🌡️ Environmental	weather, temperature, precipitation, air quality	Environmental drivers of disease emergence and transmission
🌿 Ecological	land use, biodiversity, vector habitats	Context for zoonotic spillover and vector-borne disease
🛰️ Earth Observation	satellite imagery, remote sensing	Independent, global, high-frequency observations of environmental change
🏙️ Socio-economic	population density, healthcare access, vulnerability indicators	Determines who is at risk and who is most affected

Data governance

All data used in GeoAI4EI is governed by strict legal and ethical rules:

• GDPR compliance for all personal data
• Data Sharing Agreements with every data provider
• Anonymisation standards applied before data is processed or shared
• FAIR principles (Findable, Accessible, Interoperable, Reusable) for the data infrastructure

note

Sensitive data is never published. Only anonymised, aggregated, or appropriately licensed data is used in the open-source components.

How the data is used

1. Ingest & harmonise — sources are aligned to common spatial and temporal references
2. Model — geospatial and epidemiological models consume the fused picture
3. Analyse with AI — machine learning and LLM-based assistants explore patterns and answer questions
4. Communicate — results are presented with uncertainty visualisation and traceable sources

See Technology & AI for the modelling and AI stack, and Ethics & Governance for the rules that govern all of it.