AI4Pandemics is a collaborative initiative focused on harnessing responsible artificial intelligence to prevent, prepare for, and respond to infectious disease outbreaks. We bring together researchers, public health experts, engineers, and community partners to translate data into timely insights—so leaders and frontline teams can act faster, smarter, and more equitably.
Our Mission
To advance open, ethical, and human-centered AI that strengthens global health security—reducing outbreak impact through earlier detection, better resource planning, and clear, actionable guidance.
Our Vision
A world where communities, clinics, and countries can anticipate health threats before they escalate—supported by trustworthy AI, transparent data, and collaborative science.
What We Do
- Early warning and forecasting: Build models for outbreak detection, transmission forecasting, and scenario planning to support rapid decision-making.
- Public health intelligence: Transform fragmented data (clinical, mobility, environmental, genomic, and social signals) into meaningful dashboards and alerts.
- Resource optimization: Help health systems plan capacity (beds, staff, supplies) and coordinate response at local and national levels.
- Decision support tools: Deliver user-friendly products for policymakers, hospitals, labs, and community organizations—grounded in real-world needs.
- Open science and training: Share datasets, models, and reproducible methods; host workshops to upskill teams in responsible AI for health.
- Equity and access: Design for low-resource settings, multilingual contexts, and privacy-preserving data workflows.
Core Principles
- Human-first: AI augments people—not replaces them. We co-design with practitioners and communities.
- Ethics and privacy: We apply robust governance, differential privacy where appropriate, and transparent model documentation.
- Scientific rigor: Peer-reviewed methods, validation across diverse contexts, and continuous model monitoring.
- Openness: Preference for open data, open models, and open evaluation to accelerate impact and trust.
- Equity: Solutions must work beyond well-resourced settings, addressing data gaps and structural barriers.
Focus Areas
- Syndromic surveillance and anomaly detection
- Nowcasting/forecasting for cases, admissions, and demand
- Genomic epidemiology support and variant tracking
- Health communication insights (mis/disinformation monitoring)
- Logistics and supply resilience (PPE, vaccines, therapeutics)
- Climate and environmental health signals related to outbreaks
Who We Serve
- Public health agencies and emergency operations centers
- Hospitals and health systems
- Laboratories and genomic surveillance networks
- Nonprofits, NGOs, and community-based organizations
- Multilateral and academic partners
Our Approach
- Co-creation: Start with user needs; iterate in the field.
- Interoperability: Standards-based data pipelines (APIs, FHIR/HL7 where applicable).
- Responsible AI: Bias assessments, fairness metrics, and clear model cards.
- Usability: Clear visualizations and plain-language insights that support rapid action.
Impact Highlights
- Faster situational awareness through fused data streams and risk signals.
- Improved surge planning with hospital-ready forecasting tools.
- Open benchmarks enabling apples-to-apples evaluation across regions and methods.
Get Involved
- Partnerships: Collaborate on pilots, data-sharing agreements, and joint research.
- Researchers: Contribute models, methods, and peer review.
- Practitioners: Co-design and test tools in real-world workflows.
- Supporters: Sponsor open tools, training, and deployments in underserved regions.