Implementation of AESOP early-warning system for respiratory disease: a pilot and validation study using routinely...

Respiratory infections cause major morbidity and mortality globally, highlighting the need for robust early-warning systems (EWS). The Alert-Early System of Outbreaks with Pandemic Potential (ÆSOP) was co-developed with surveillance stakeholders to detect outbreaks using administrative primary health care (PHC) data. We aimed to report ÆSOP's feasibility and performance for detecting influenza-like illness (ILI) outbreaks in a real-world context and to document lessons learned from translating research into routine health services.

The pilot was conducted in Amazonas, Brazil, from April to July 2024, covering 62 municipalities. ÆSOP relies on anomaly detection models applied to weekly counts of ILI-related PHC encounters, and municipal-level warnings are issued when thresholds are exceeded. Local health authorities validated warnings through a structured questionnaire. Performance was evaluated by sensitivity, specificity, positive and negative predictive value (PPV and NPV).

During the study period, 1.8 million PHC encounters were reported, of which 6.6% were ILI-related. ÆSOP issued 104 warnings across 41 municipalities. Sensitivity was 62.8% (95% CI 54.4%-70.5%), specificity 95.5% (95% CI 93.1%-97.1%), PPV 82.7%, (95% CI 74.3%-88.8%), and NPV 88.1% (95% CI 84.7%-90.9%). In 72% of confirmed outbreaks, ÆSOP provided the first signal to authorities, and in 56%, warnings supported responses including enhanced risk communication, equipment/test deployment, and strengthening of intergovernmental coordination.

ÆSOP anticipated outbreaks in real-world usage and was validated by authorities as a decision-support tool. Its reliance on administrative data and open-source technologies enhances scalability and adaptability across health systems globally.

Rockefeller Foundation (award 2023 PPI 007).