title: A robust parameter estimation method for estimating disease burden of respiratory viruses.
abstract: BACKGROUND
  Poisson model has been widely applied to estimate the disease burden of influenza, but there has been little success in providing reliable estimates for other respiratory viruses.
  
  
  METHODS
  We compared the estimates of excess hospitalization rates derived from the Poisson models with different combinations of inference methods and virus proxies respectively, with the aim to determine the optimal modeling approach. These models were validated by comparing the estimates of excess hospitalization attributable to respiratory viruses with the observed rates of laboratory confirmed paediatric hospitalization for acute respiratory infections obtained from a population based study.
  
  
  RESULTS
  The Bayesian inference method generally outperformed the classical likelihood estimation, particularly for RSV and parainfluenza, in terms of providing estimates closer to the observed hospitalization rates. Compared to the other proxy variables, age-specific positive counts provided better estimates for influenza, RSV and parainfluenza, regardless of inference methods. The Bayesian inference combined with age-specific positive counts also provided valid and reliable estimates for excess hospitalization associated with multiple respiratory viruses in both the 2009 H1N1 pandemic and interpandemic period.
  
  
  CONCLUSIONS
  Poisson models using the Bayesian inference method and virus proxies of age-specific positive counts should be considered in disease burden studies on multiple respiratory viruses.
authors.affiliation: School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: King Pan
authors.initials: KP
authors.lastname: Chan
authors.affiliation: School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: Chit Ming
authors.initials: CM
authors.lastname: Wong
authors.affiliation: Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: Susan S S
authors.initials: SS
authors.lastname: Chiu
authors.affiliation: Department of Microbiology, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: Kwok Hung
authors.initials: KH
authors.lastname: Chan
authors.affiliation: School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: Xi Ling
authors.initials: XL
authors.lastname: Wang
authors.affiliation: Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
authors.forename: Eunice L Y
authors.initials: EL
authors.lastname: Chan
authors.affiliation: School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China; HKU - Pasteur Research Centre, Hong Kong Special Administrative Region, China.
authors.forename: J S Malik
authors.initials: JS
authors.lastname: Peiris
authors.affiliation: School of Publish Health, The University of Hong Kong, Hong Kong Special Administrative Region, China; Squina International Centre for Infection Control, School of Nursing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D017281:Cost of Illness; D004812:Epidemiologic Methods; D006760:Hospitalization; D006801:Humans; D007223:Infant; D016012:Poisson Distribution; D012141:Respiratory Tract Infections / Q000821:virology*; D012621:Seasons; D014780:Viruses / Q000472:pathogenicity*