Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data.

ABSTRACT: Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. Surveillance indicators routinely collected to monitor these trends are often a composite of multiple pathogens. For example, "influenza-like illness"-routinely monitored as a proxy for influenza infections-is a symptom definition that could be caused by a wide range of pathogens, including multiple subtypes of influenza, SARS-CoV-2, and RSV. Inferred trends from such composite time series may not reflect the trends of any one of the component pathogens, each of which can exhibit distinct dynamics. Although many surveillance systems routinely test a subset of individuals contributing to a surveillance indicator-providing information on the relative contrib

SUBMITTER: Eales O 

PROVIDER: S-EPMC12799599 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature