title: Joint modelling of serological and hospitalization data reveals that high levels of pre-existing immunity and school holidays shaped the influenza A pandemic of 2009 in the Netherlands.
abstract: Obtaining a quantitative understanding of the transmission dynamics of influenza A is important for predicting healthcare demand and assessing the likely impact of intervention measures. The pandemic of 2009 provides an ideal platform for developing integrative analyses as it has been studied intensively, and a wealth of data sources is available. Here, we analyse two complementary datasets in a disease transmission framework: cross-sectional serological surveys providing data on infection attack rates, and hospitalization data that convey information on the timing and duration of the pandemic. We estimate key epidemic determinants such as infection and hospitalization rates, and the impact of a school holiday. In contrast to previous approaches, our novel modelling of serological data with mixture distributions provides a probabilistic classification of individual samples (susceptible, immune and infected), propagating classification uncertainties to the transmission model and enabling serological classifications to be informed by hospitalization data. The analyses show that high levels of immunity among persons 20 years and older provide a consistent explanation of the skewed attack rates observed during the pandemic and yield precise estimates of the probability of hospitalization per infection (1-4 years: 0.00096 (95%CrI: 0.00078-0.0012); 5-19 years: 0.00036 (0.00031-0.0044); 20-64 years: 0.0015 (0.00091-0.0020); 65+ years: 0.0084 (0.0028-0.016)). The analyses suggest that in The Netherlands, the school holiday period reduced the number of infectious contacts between 5- and 9-year-old children substantially (estimated reduction: 54%; 95%CrI: 29-82%), thereby delaying the unfolding of the pandemic in The Netherlands by approximately a week.
authors.affiliation: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands.
authors.forename: Dennis E
authors.initials: DE
authors.lastname: Te Beest
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, CB2 0SR.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Birrell
authors.affiliation: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands.
authors.forename: Jacco
authors.initials: J
authors.lastname: Wallinga
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, CB2 0SR.
authors.forename: Daniela
authors.initials: D
authors.lastname: De Angelis
authors.affiliation: Centre for Infectious Disease Control, National Institute for Public Health and the Environment, PO Box 1, Bilthoven 3720AB, The Netherlands michiel.van.boven@rivm.nl.
authors.forename: Michiel
authors.initials: M
authors.lastname: van Boven
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D005260:Female; D006760:Hospitalization; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000276:immunology / Q000635:transmission; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D009426:Netherlands / Q000453:epidemiology; D058873:Pandemics
keywords: Bayesian evidence synthesis; hospitalization incidence; influenza A; mixture analysis; serology; transmission model