Modelling infectious disease transmission with complex exposure pattern and sparse outcome data

Marie Reilly*, Agus Salim, Emer Lawlor, Owen Smith, Ian Temperley, Yudi Pawitan

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    We present a regression modelling framework to analyse infectious disease transmission during a time period where extensive exposure data are available, but where the outcome data are sparse. A latent variable model is used for each exposure time, allowing a straight-forward accumulation of risk for a collection of exposures for which outcome data are available. We describe an analysis of HIV infection from blood products among a cohort of haemophiliacs in Ireland between 1980 and 1985. The analysis provides estimates of the time pattern and batch effects; we show how analytical complexity such as smoothly varying coefficients or random coefficient models can be accommodated by the model. Finally, we discuss other problems where the model is applicable.

    Original languageEnglish
    Pages (from-to)3013-3032
    Number of pages20
    JournalStatistics in Medicine
    Volume23
    Issue number19
    DOIs
    Publication statusPublished - 15 Oct 2004

    Fingerprint

    Dive into the research topics of 'Modelling infectious disease transmission with complex exposure pattern and sparse outcome data'. Together they form a unique fingerprint.

    Cite this