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TRUVACE RECORD VERSION
record: TRV-2026-1177
version: 2
kind: revised
reason: Direction audit: filed as a problem but describes a gain.
timestamp: 2026-10-26T06:38:41.295733Z
status: published
lens: g_space
sector: health
headline: Leveraging AI for infectious disease modelling and public health decision making
dek: Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions. The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness. Examples inclu…
gain_title: AI is expanding public health systems' capacity to detect disease signals, forecast outbreaks, and support operational decisions.
problem_title: (none)
trace_subject: (none)
gain_reading: AI is expanding public health systems' capacity to detect disease signals, forecast outbreaks, and support operational decisions.
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions. The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness.

Examples included genomic surveillance, hybrid epidemiological and machine-learning models, spatial foundation models, AI-enabled decision support, and agent-based simulations for resource allocation. Sustained interdisciplinary collaboration, human oversight, robust validation, and equitable design will be essential to embed AI safely and effectively within public health decision-making.
limitation: 
tag: Evidence-backed gain
key_points: The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness. | Examples included genomic surveillance, hybrid epidemiological and machine-learning models, spatial foundation models, AI-enabled decision support, and agent-based simulations for resource allocation. | Participants emphasised that technical performance alone is insufficient: tools must be transparent, auditable, transferable across settings, operationally usable, and responsive to local data and infrastructure constraints.
rundown: Artificial intelligence (AI) is expanding the capacity of public health systems to detect infectious disease signals, forecast outbreaks, analyse pathogen evolution, generate localised risk estimates, and support operational decisions. The thirteenth session of the WHO Pandemic and Epidemic Intelligence Innovation Forum brought together experts from academic, public health, and technology organisations to examine current applications of AI in infectious disease modelling and pandemic preparedness.

Examples included genomic surveillance, hybrid epidemiological and machine-learning models, spatial foundation models, AI-enabled decision support, and agent-based simulations for resource allocation. Participants emphasised that technical performance alone is insufficient: tools must be transparent, auditable, transferable across settings, operationally usable, and responsive to local data and infrastructure constraints.
sources:
- peer_reviewed | BMC Proceedings | https://doi.org/10.1186/s12919-026-00398-w | 2026-09-21
prev: d7b1ccc2e18c295cc0808a0f577c6407bedeb16eb247ba3e408d8c7d2ae95bab

SHA-256 hash

5272bd324b93d0e95d0358245e2bd48186d91d4cea3e7e9775d3304b45b34cb4

Previous version’s hash

d7b1ccc2e18c295cc0808a0f577c6407bedeb16eb247ba3e408d8c7d2ae95bab