title: Uses of the EM algorithm in the analysis of data on HIV/AIDS and other infectious diseases.
abstract: The analysis of data on infectious diseases is a natural setting for applications of the EM algorithm, because the infection process is only partially observable. Difficulties in determining the expectation at the E step have been side-stepped by adopting pragmatic models which reflect only part of the mechanism that generates the data. In the HIV/AIDS context the EM algorithm has helped in the reconstruction of the unobserved HIV infection curve, the so-called backprojection problem, as well as in the estimation of the distribution for the incubation period until AIDS, in estimating the infectivity of HIV in partnerships and in estimating parameters describing the decline in the immune system. There is a need for smooth estimates of functions in these applications, suggesting the use of the EMS algorithm or use of the EM algorithm to maximize a penalized likelihood. For data on other infectious diseases the application of the EM algorithm has so far been restricted to analyses of data on the size of outbreaks in a sample of households.
authors.affiliation: School of Mathematical and Statistical Sciences, La Trobe University, Bundoora, Australia. n.becker@latrobe.edu.au
authors.forename: N G
authors.initials: NG
authors.lastname: Becker
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000453:epidemiology* / Q000276:immunology / Q000635:transmission; D000465:Algorithms; D001699:Biometry / Q000379:methods; D018791:CD4 Lymphocyte Count; D003141:Communicable Diseases / Q000453:epidemiology* / Q000276:immunology / Q000635:transmission; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D015658:HIV Infections / Q000453:epidemiology* / Q000635:transmission; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological