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AIDS: modelling and predicting

Published online by Cambridge University Press:  04 August 2010

Valerie Isham
Affiliation:
University College London
Graham Medley
Affiliation:
University of Warwick
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Summary

Introduction

AIDS continues to place enormous demands on health-care resources and it is essential for public health planning that useful estimates are available of current and future numbers of individuals at different stages of HIV disease. People with HIV infection are eligible to receive treatments at ever earlier stages of the disease, and accurate estimates are required to ensure adequate resources are available. People sick with advanced HIV disease may be in need of special care. Estimates are also crucial for developing policy on awareness campaigns and intervention programs, as well as for investigating the value of needle exchange and other prevention, including vaccination, programs.

Many unanswered questions about the epidemic are essentially statistical in nature, for despite efforts over the past decade to improve both the collection and quality of data on HIV and AIDS, the data are still often incomplete, and there remain large gaps in our knowledge on many key epidemiological parameters.

In particular, the infectivity of HIV is a fundamental unknown and there is uncertainty about the incubation period and its space-time trends. The available data are therefore an incomplete description of phenomena which are, on the whole, relatively poorly understood, and predictions of the epidemic based on the available data are subject to considerable uncertainty. This uncertainty makes AIDS grimly interesting to statisticians, but the prediction problems have been forced upon us because of their practical urgency, regardless of whether or not we can solve them. The role of markers such as CD4 cell counts, IgA and other markers in HIV disease is currently receiving considerable attention by AIDS researchers and statisticians. A further major uncertainty is that of treatment efficacy.

Type
Chapter
Information
Models for Infectious Human Diseases
Their Structure and Relation to Data
, pp. 407 - 438
Publisher: Cambridge University Press
Print publication year: 1996

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