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. 2018 Jun 26;18(1):790.
doi: 10.1186/s12889-018-5671-7.

Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Alice Corbella  1 , Xu-Sheng Zhang  2 , Paul J Birrell  3 , Nicki Boddington  2 , Richard G Pebody  2 , Anne M Presanis  2 , Daniela De Angelis  3   2
Affiliations

Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Alice Corbella et al. BMC Public Health. .

Abstract

Background: 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.

Methods: 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 admissions to intensive care is possible.

Results: 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 the Christmas school holiday on disease spread during seasons 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.

Conclusion: 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.

Keywords: Bayesian inference; Epidemic models; Epidemic monitoring; Influenza; Reproduction number; Severe cases.

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Conflict of interest statement

Ethics approval and consent to participate

AC accessed data held at Public Health England (PHE) as a PHE employee, under an honorary contract.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Weekly ICU/HDU admissions by season. Time is measured in week number as reported on the x axis
Fig. 2
Fig. 2
The model. Schematic diagram representing the epidemic model and the model linking transmission to ICU/HDU admissions (in blue)
Fig. 3
Fig. 3
Retrospective analysis, uninformative scenario. Prior (red) and posterior (blue) distributions of: the initial susceptibility (π); the over-dispersion parameter (η); the probability of ICU admission given infection (pICU); the scaling parameter (κ); and the basic and effective reproduction number (R0 and Rn). The results are derived from season 2012/13 (left column), season 2013/14 (centre) and season 2014/15 (right column)
Fig. 4
Fig. 4
Retrospective analysis, uninformative scenario. Median (blue), 95% CrI (light green) and quartile (dark green) of the posterior predictive distributions and observed values (red) for the weekly ICU/HDU admissions across seasons. The vertical dashed lines represent the breakpoints for the piecewise transmissibility β∗(t) (i.e. start and end of each school holiday)
Fig. 5
Fig. 5
Prospective analysis, informative scenario. The black line displays the analysis time; the blue line and green shaded area represent median, quartile (dark green) and 95% CrIs (light green) of the posterior predictive distribution for the training dataset weeks. The pink area displays posterior quartiles (deep pink) and 95% CrIs (light pink) for the predicted future observations, and the purple line displays the median; the red dots are the training data and the yellow dots are the observations we have predicted
See this image and copyright information in PMC

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