Modelling infectious disease transmission with complex exposure pattern and sparse outcome data.
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.
Reilly
Marie
M
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. marie.reilly@meb.ki.se
Salim
Agus
A
Lawlor
Emer
E
Smith
Owen
O
Temperley
Ian
I
Pawitan
Yudi
Y
D015331:Cohort Studies; D003141:Communicable Diseases / Q000635:transmission*; D018562:Disease Transmission, Infectious; D015658:HIV Infections / Q000635:transmission; D006467:Hemophilia A / Q000628:therapy; D006801:Humans; D007494:Ireland; D008954:Models, Biological; D015233:Models, Statistical; D065227:Transfusion Reaction