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

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.

Citation:
Eales O, Windecker SM, McCaw JM, Shearer FM. Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data. Am J Epidemiol. 2025;194(9):2489-98.

Keywords:
Bayesian time-series analysis; SARS-CoV-2 variants; dengue serotypes; influenza subtypes; pathogen dynamics; statistical modeling

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.