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

Marie Reilly·Agus Salim·Emer Lawlor·Owen Smith·Ian Temperley·Yudi Pawitan
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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.

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Cohort StudiesCommunicable DiseasesDisease Transmission, InfectiousHIV InfectionsHemophilia AHumansIrelandModels, BiologicalModels, StatisticalTransfusion ReactionJournal Article

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Marie Reilly, Agus Salim, Emer Lawlor, Owen Smith, Ian Temperley, Yudi Pawitan. 2004-10-15. Modelling infectious disease transmission with complex exposure pattern and sparse outcome data.. https://doi.org/10.1002/sim.1881

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