RT Journal Article
SR Electronic
T1 Connecting surveillance and population-level influenza incidence
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 427708
DO 10.1101/427708
A1 Robert C. Cope
A1 Joshua V. Ross
A1 Monique Chilver
A1 Nigel P. Stocks
A1 Lewis Mitchell
YR 2018
UL http://biorxiv.org/content/early/2018/09/28/427708.abstract
AB There is substantial interest in estimating and forecasting influenza incidence. Surveillance of influenza is challenging as one needs to demarcate influenza from other respiratory viruses, and due to asymptomatic infections. To circumvent these challenges, surveillance data often targets influenza-like-illness, or uses context-specific normalisations such as test positivity or per-consultation rates. Specifically, influenza incidence itself is not reported. We propose a framework to estimate population-level influenza incidence, and its associated uncertainty, using surveillance data and hierarchical observation processes. This new framework, and forecasting and forecast assessment methods, are demonstrated for three Australian states over 2016 and 2017. The framework allows for comparison within and between seasons in which surveillance effort has varied. Implementing this framework would improve influenza surveillance and forecasting globally, and could be applied to other diseases for which surveillance is difficult.