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TRUVACE RECORD VERSION
record: TRV-2026-1177
version: 4
kind: revised
reason: Voice pass: plainer wording and study attribution; no facts or figures changed.
timestamp: 2026-11-05T11:20:08.056683Z
status: published
lens: general
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, thorough validation, and equitable design will be essential to embed AI safely and effectively within public health decision-making.
limitation: 
tag: Context
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: 0b77524ab9021894980a52d0a312c3ff18c4e8cf08f9785b447ccfdaaf9d198b

SHA-256 hash

159e4759a4d9a4dd516b6aef72c2c758cd9194f82a6a7155228291db8541c9e8

Previous version’s hash

0b77524ab9021894980a52d0a312c3ff18c4e8cf08f9785b447ccfdaaf9d198b