Title: Exploiting routinely collected severe case data to monitor and predict influenza outbreaks,
Abstract: Influenza remains a significant burden on health systems. Effective responses
rely on the timely understanding of the magnitude and the evolution of an
outbreak. For monitoring purposes, data on severe cases of influenza in England
are reported weekly to Public Health England. These data are both readily
available and have the potential to provide valuable information to estimate
and predict the key transmission features of seasonal and pandemic influenza.
We propose an epidemic model that links the underlying unobserved influenza
transmission process to data on severe influenza cases. Within a Bayesian
framework, we infer retrospectively the parameters of the epidemic model for
each seasonal outbreak from 2012 to 2015, including: the effective reproduction
number; the initial susceptibility; the probability of admission to intensive
care given infection; and the effect of school closure on transmission. The
model is also implemented in real time to assess whether early forecasting of
the number of admission to intensive care is possible. Our model of admissions
data allows reconstruction of the underlying transmission dynamics revealing:
increased transmission during the season 2013/14 and a noticeable effect of
Christmas school holiday on disease spread during season 2012/13 and 2014/15.
When information on the initial immunity of the population is available,
forecasts of the number of admissions to intensive care can be substantially
improved. Readily available severe case data can be effectively used to
estimate epidemiological characteristics and to predict the evolution of an
epidemic, crucially allowing real-time monitoring of the transmission and
severity of the outbreak.