title: Predicting the epidemic sizes of influenza A/H1N1, A/H3N2, and B: a statistical method.
abstract: BACKGROUND
  The epidemic sizes of influenza A/H3N2, A/H1N1, and B infections vary from year to year in the United States. We use publicly available US Centers for Disease Control (CDC) influenza surveillance data between 1997 and 2009 to study the temporal dynamics of influenza over this period.
  
  
  RESULTS
  Regional outpatient surveillance data on influenza-like illness (ILI) and virologic surveillance data were combined to define a weekly proxy for the incidence of each strain in the United States. All strains exhibited a negative association between their cumulative incidence proxy (CIP) for the whole season (from calendar week 40 of each year to calendar week 20 of the next year) and the CIP of the other two strains (the complementary CIP) from the start of the season up to calendar week 2 (or 3, 4, or 5) of the next year. We introduce a method to predict a particular strain's CIP for the whole season by following the incidence of each strain from the start of the season until either the CIP of the chosen strain or its complementary CIP exceed certain thresholds. The method yielded accurate predictions, which generally occurred within a few weeks of the peak of incidence of the chosen strain, sometimes after that peak. For the largest seasons in the data, which were dominated by A/H3N2, prediction of A/H3N2 incidence always occurred at least several weeks in advance of the peak.
  
  
  CONCLUSIONS
  Early circulation of one influenza strain is associated with a reduced total incidence of the other strains, consistent with the presence of interference between subtypes. Routine ILI and virologic surveillance data can be combined using this new method to predict the relative size of each influenza strain's epidemic by following the change in incidence of a given strain in the context of the incidence of cocirculating strains. Please see later in the article for the Editors' Summary.
authors.affiliation: Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America. egoldste@hsph.harvard.edu
authors.forename: Edward
authors.initials: E
authors.lastname: Goldstein
authors.forename: Sarah
authors.initials: S
authors.lastname: Cobey
authors.forename: Saki
authors.initials: S
authors.lastname: Takahashi
authors.forename: Joel C
authors.initials: JC
authors.lastname: Miller
authors.forename: Marc
authors.initials: M
authors.lastname: Lipsitch
mesh_terms: D002487:Centers for Disease Control and Prevention, U.S.; D058872:Epidemics / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D015994:Incidence; D053118:Influenza A Virus, H1N1 Subtype / Q000472:pathogenicity; D053122:Influenza A Virus, H3N2 Subtype / Q000472:pathogenicity; D009981:Influenza B virus / Q000472:pathogenicity; D007251:Influenza, Human / Q000453:epidemiology* / Q000821:virology; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D012621:Seasons; D014481:United States / Q000453:epidemiology