Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement
Oliver Eales, James M McCaw, Freya M Shearer · 2024
Read Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement by Oliver Eales, James M McCaw, Freya M Shearer (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11608885.txt.
Description
Eales, McCaw, and Shearer present a study from twenty twenty-four on flaws in routine flu tracking. Health teams infer flu spread from proxy signals, since true infection counts are hard to get. The team use basic math to link three common proxies to real infection rates. Those proxies are illness reports at sentinel clinics, lab-confirmed case rates, and the share of tests that come back positive. Their analysis shows none of the three tracks infection rates reliably.
Other germs with like symptoms can sway the proxies heavily. Shifts in testing rates and in who seeks care add more bias. Age groups differ in infection, symptoms, and care seeking, which skews trends. The authors give six practical fixes to improve flu monitoring.
Catalog record
| CREATOR | Oliver Eales, James M McCaw, Freya M Shearer |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |