2017/06/08 by Alice Corbella, Corbella, Alice, Xu‐Sheng Zhang +10
Medicine · Mathematics · #Influenza Virus Research Studies #COVID-19 epidemiological studies
paper · pdf · doi:10.48550/arxiv.1706.02527
Influenza remains a significant burden on health systems. Effective responses\nrely on the timely understanding of the magnitude and the evolution of an\noutbreak. For monitoring purposes, data on severe cases of influenza in England\nare reported weekly to Public Health England. These data are both readily\navailable and have the potential to provide valuable information to estimate\nand predict the key transmission features of seasonal and pandemic influenza.\nWe propose an epidemic model that links the underlying unobserved influenza\ntransmission process to data on severe influenza cases. Within a Bayesian\nframework, we infer retrospectively the parameters of the epidemic model for\neach seasonal outbreak from 2012 to 2015, including: the effective reproduction\nnumber; the initial susceptibility; the probability of admission to intensive\ncare given infection; and the effect of school closure on transmission. The\nmodel is also implemented in real time to assess whether early forecasting of\nthe number of admission to intensive care is possible. Our model of admissions\ndata allows reconstruction of the underlying transmission dynamics revealing:\nincreased transmission during the season 2013/14 and a noticeable effect of\nChristmas school holiday on disease spread during season 2012/13 and 2014/15.\nWhen information on the initial immunity of the population is available,\nforecasts of the number of admissions to intensive care can be substantially\nimproved. Readily available severe case data can be effectively used to\nestimate epidemiological characteristics and to predict the evolution of an\nepidemic, crucially allowing real-time monitoring of the transmission and\nseverity of the outbreak.\n