ArticleBMC public health2018
Exploiting routinely collected severe case data to monitor and predict influenza outbreaks.
Article in BMC public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Forecasting of influenza activity and associated hospital admission burden and estimating the impact of COVID-19 pandemic on 2019/20 winter season in Hong Kong.PLoS computational biology · 2024Article
- Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study.BMC public health · 2020Article
- Bayesian modelling of a hepatitis A outbreak in men who have sex with men in Sydney, Australia, 1991/1992.Epidemiology and infection · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundInfluenza 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.
methodsWe 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.
resultsOur 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.
conclusionReadily 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.
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