Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement DOI
Oliver Eales, James M. McCaw, Freya M. Shearer

et al.

Influenza and Other Respiratory Viruses, Journal Year: 2024, Volume and Issue: 18(12)

Published: Dec. 1, 2024

ABSTRACT Background Monitoring how the incidence of influenza infections changes over time is important for quantifying transmission dynamics and clinical severity influenza. Infection difficult to measure directly, hence, other quantities which are more amenable surveillance used monitor trends in infection levels, with implicit assumption that they correlate incidence. Methods Here, we demonstrate, through mathematical reasoning using fundamental principles, relationship between three commonly reported indicators: (1) rate per unit influenza‐like illness sentinel healthcare sites, (2) laboratory‐confirmed (3) proportion laboratory tests positive (‘test‐positive proportion’). Results Our analysis suggests none these ubiquitously indicators a reliable tool monitoring In particular, highlight can be heavily biassed by following: circulating pathogens (other than influenza) similar symptom profiles, testing rates differences rates, healthcare‐seeking behaviour age‐groups time. We make six practical recommendations improve The implementation our would enable construction interpretable indicator(s) from underlying patterns could readily monitored. Conclusions all (or subset) greatly understanding dynamics, burden influenza, improving ability respond effectively seasonal epidemics future pandemics.

Language: Английский

Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement DOI
Oliver Eales, James M. McCaw, Freya M. Shearer

et al.

Influenza and Other Respiratory Viruses, Journal Year: 2024, Volume and Issue: 18(12)

Published: Dec. 1, 2024

ABSTRACT Background Monitoring how the incidence of influenza infections changes over time is important for quantifying transmission dynamics and clinical severity influenza. Infection difficult to measure directly, hence, other quantities which are more amenable surveillance used monitor trends in infection levels, with implicit assumption that they correlate incidence. Methods Here, we demonstrate, through mathematical reasoning using fundamental principles, relationship between three commonly reported indicators: (1) rate per unit influenza‐like illness sentinel healthcare sites, (2) laboratory‐confirmed (3) proportion laboratory tests positive (‘test‐positive proportion’). Results Our analysis suggests none these ubiquitously indicators a reliable tool monitoring In particular, highlight can be heavily biassed by following: circulating pathogens (other than influenza) similar symptom profiles, testing rates differences rates, healthcare‐seeking behaviour age‐groups time. We make six practical recommendations improve The implementation our would enable construction interpretable indicator(s) from underlying patterns could readily monitored. Conclusions all (or subset) greatly understanding dynamics, burden influenza, improving ability respond effectively seasonal epidemics future pandemics.

Language: Английский

Citations

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