title: Characterising pandemic severity and transmissibility from data collected during first few hundred studies.
abstract: Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by "First Few Hundred" (FF100) studies, which involve surveillance-possibly in person, or via telephone-of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved. We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.
authors.affiliation: School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia; ACEMS, School of Mathematical Sciences, University of Adelaide, Adelaide, SA 5005, Australia. Electronic address: andrew.black@adelaide.edu.au.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Black
authors.affiliation: Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; School of Computing and Information Systems, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Geard
authors.affiliation: Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC 3010, Australia; Murdoch Childrens Research Institute, Royal Childrens Hospital, VIC, Australia.
authors.forename: James M
authors.initials: JM
authors.lastname: McCaw
authors.affiliation: Center for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia; Murdoch Childrens Research Institute, Royal Childrens Hospital, VIC, Australia; The Peter Doherty Institute for Infection and Immunity, The University of Melbourne and Royal Melbourne Hospital, Melbourne, VIC 3000, Australia.
authors.forename: Jodie
authors.initials: J
authors.lastname: McVernon
authors.affiliation: School of Mathematical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia; ACEMS, School of Mathematical Sciences, University of Adelaide, Adelaide, SA 5005, Australia.
authors.forename: Joshua V
authors.initials: JV
authors.lastname: Ross
mesh_terms: D001315:Australia / Q000453:epidemiology; D001499:Bayes Theorem; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D008390:Markov Chains; D058873:Pandemics / Q000706:statistics & numerical data*
keywords: Households; Influenza; Markov chain; Pandemic; Parameter inference