Stochastic methods for short term projections of symptomatic HIV disease.
We designed and implemented stochastic methods for short term projections of HIV disease at the local level, that accommodate various states or stages of the disease. We gave particular attention to projection of the number of patients with HIV disease who need care, when durations of stay in these various states depend on current methods for treating opportunistic infections. We consider two types of data as input to these projections. One concerns seroprevalence surveys conducted over time and from which we can obtain time series estimates of the numbers of HIV-infected individuals. The other is a reported time series of AIDS cases adjusted for delays in reporting. Several projections, with data from the City of Philadelphia, illustrate this method. In addition, we consider a Monte Carlo method for computing confidence bounds on a projection.
Department of Mathematics and Computer Science, Drexel University, Philadelphia, Pa. 19104.
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D016001:Confidence Intervals; D005544:Forecasting; D015658:HIV Infections / Q000188:drug therapy / Q000453:epidemiology*; D015528:HIV Seroprevalence; D006801:Humans; D009010:Monte Carlo Method; D015143:Philadelphia / Q000453:epidemiology; D013269:Stochastic Processes; D015215:Zidovudine / Q000627:therapeutic use
D015215:Zidovudine