Predicting the course of AIDS in Australia.

There have been urgent demands for knowledge about the epidemic of the acquired immunodeficiency syndrome (AIDS) in Australia. Accurate predictions are important for efficient allocation and planning of limited health-care resources. Ideal data for this purpose would be reliable knowledge of the past and present incidence of human immunodeficiency virus (HIV) infection. However, since the incidence of the infection is unknown predictions can only be based on historical data of the incidence of AIDS. In this article, we show the limitations of such predictions by examining a broad range of mathematical models that successfully track the observed data (1187 cases diagnosed to December 31, 1988). In addition, we describe a simple method for prediction in subgroups where the numbers of cases observed so far are small. Four models representing different forms of departure from the simple exponential model provide the best fits to the Australian AIDS data. Regional variability and a possible effect resulting from the introduction of zidovudine were incorporated into the models. Significant regional variability in the course of the epidemic was observed between New South Wales, Victoria and the rest of the country. For Australia as a whole, the doubling time changed from less than one year before mid 1987 to more than two years after this time. Model fits were improved by fitting the models to just the four years of data from 1985. The models give comparable predictions for the first year (1989) of around 600 new cases. However, by 1993 the predictions vary considerably, ranging from 500 to 2300 new cases. It is predicted that between 3100 and 6700 cases are likely to be diagnosed in Australia between 1989 and 1993. The results from the subgroup prediction demonstrate that when the observed number of cases is small, then the range of predictions for a future time interval is very wide. For reliable long-term predictions that are necessary for public health planning, basic information on the past and present incidence of HIV infection is urgently needed.