title: Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in the Netherlands.
abstract: During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity.
authors.affiliation: National Institute for Public Health and Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands. tjibbe.donker@rivm.nl
authors.forename: Tjibbe
authors.initials: T
authors.lastname: Donker
authors.forename: Michiel
authors.initials: M
authors.lastname: van Boven
authors.forename: W Marijn
authors.initials: WM
authors.lastname: van Ballegooijen
authors.forename: Tessa M
authors.initials: TM
authors.lastname: Van't Klooster
authors.forename: Cornelia C
authors.initials: CC
authors.lastname: Wielders
authors.forename: Jacco
authors.initials: J
authors.lastname: Wallinga
mesh_terms: D000465:Algorithms; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology*; D009426:Netherlands / Q000453:epidemiology; D012189:Retrospective Studies