Leveraging the power of digital health to fight pandemics: The example of ÆSOP.

Emerging outbreaks highlight the need for early warning systems, but low-resource centers often face challenges to maintain surveillance capabilities. Administrative data-based systems offer a cost-efficient approach to strengthening surveillance. The present study evaluated whether a primary health care (PHC)-based early warning system could anticipate respiratory outbreak detection, when compared to traditional surveillance. Weekly counts of influenza-like illness PHC encounters in Rio de Janeiro were analyzed from October 2019 to May 2020 and from October 2021 to May 2022. PHC data was compared to weekly surveillance notifications and used time series regression to estimate predicted counts of PHC encounters. Subsequent outbreak warnings were then issued. Our study identified 659,230 influenza-like illness PHC encounters in the first period, and 702,886 in the second period. In the first period, PHC data deviated from baseline two weeks before the rise in notifications during the first COVID-19 wave and one week earlier in the second period. The PHC-based system successfully triggered warnings capable of anticipating the surveillance system. Our findings show PHC-based early warning systems can anticipate outbreaks earlier than traditional surveillance, supporting their role in enhancing surveillance in low-resource settings.