title: Estimating epidemic parameters: Application to H1N1 pandemic data.
abstract: This paper discusses estimation of the parameters in an SIR epidemic model from the observed longitudinal new infection count data. The potential problems of the standard MLE approaches are revealed and possible remedies suggested. The analysis is based on the epidemic data from the 2009 outbreak of H1N1 influenza on the campus of Washington State University.
authors.affiliation: School of Biological Sciences and Department of Mathematics, Washington State University, Pullman, WA 99164, USA. Electronic address: ejs@wsu.edu.
authors.forename: Elissa J
authors.initials: EJ
authors.lastname: Schwartz
authors.affiliation: Department of Computer Science and Statistics, Daegu University, Gyeongbuk 712-714, Republic of Korea. Electronic address: bchoi@daegu.ac.kr.
authors.forename: Boseung
authors.initials: B
authors.lastname: Choi
authors.affiliation: Division of Biostatistics and Mathematical Biosciences Institute, The Ohio State University, Columbus, OH 43210, USA. Electronic address: rempala.3@osu.edu.
authors.forename: Grzegorz A
authors.initials: GA
authors.lastname: Rempala
mesh_terms: D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000472:pathogenicity*; D007251:Influenza, Human / Q000453:epidemiology*; D008962:Models, Theoretical; D058873:Pandemics / Q000706:statistics & numerical data*
keywords: H1N1 influenza; Monte Carlo approximation; Parameter estimation; Stochastic SIR model