A Framework for Evaluating the Use of Surveillance Systems for Short-Term Influenza Forecasting.

ABSTRACT:

Background

Public health surveillance systems need to monitor influenza activity and guide measures to mitigate its high impact on morbidity, mortality and healthcare systems. There is an increasing expectation that surveillance data will support the modeling of future short-term disease scenarios using artificial intelligence (AI) and machine learning (ML). This study examines how influenza surveillance can support AI/ML-based short-term forecasting for influenza at the community and hospital levels in a high-income country setting (Aotearoa/New Zealand).

Methods

This study used a two-phase approach. The first phase involved a comprehensive review of government reports, official websites, and literature to characterize existing influenza surveillance systems. The second pha

SUBMITTER: Maroufi N 

PROVIDER: S-EPMC12307093 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature