National Academies Press: OpenBook

Confronting AIDS: Update 1988 (1988)

Chapter: 3. Understanding the Course of the Epidemic

« Previous: 2. HIV Infection and its Epidemiology
Suggested Citation:"3. Understanding the Course of the Epidemic." Institute of Medicine. 1988. Confronting AIDS: Update 1988. Washington, DC: The National Academies Press. doi: 10.17226/771.
Page 57
Suggested Citation:"3. Understanding the Course of the Epidemic." Institute of Medicine. 1988. Confronting AIDS: Update 1988. Washington, DC: The National Academies Press. doi: 10.17226/771.
Page 58
Suggested Citation:"3. Understanding the Course of the Epidemic." Institute of Medicine. 1988. Confronting AIDS: Update 1988. Washington, DC: The National Academies Press. doi: 10.17226/771.
Page 59
Suggested Citation:"3. Understanding the Course of the Epidemic." Institute of Medicine. 1988. Confronting AIDS: Update 1988. Washington, DC: The National Academies Press. doi: 10.17226/771.
Page 60

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Understanding the Course of the Epidemic To alter the course of the HIV epidemic, planners must estimate, as early and as precisely as possible, how it will progress. Such predictions, like any forecasts, are necessarily based on available information, how- ever incomplete it may be. The techniques used in efforts to bridge the gaps in information are forms of mathematical modeling. This chapter describes the uses of models and potential means of sharpening the projections they provide. In 1986 the U.S. Public Health Service (PHS) projected numbers of AIDS cases and deaths through 1991. So far the reported number of cases has closely followed these projections. PHS estimated that by the end of 1991 there will have been a cumulative total of 270,000 AIDS cases in the United States and 179,000 deaths. These projections were derived from a model based on a statistical trend analysis of AIDS cases reported to CDC through May 1986, corrected for reporting delays (Morgan and Curran, 19861. Such models depend on the assumption that observed trends of the disease (e.g., the distribution of cases by age, sex, geographic location, and risk group) will not change with time. Another method to forecast short-term projections of the number of AIDS cases uses information about the period between HIV infection and AIDS, and AIDS incidence data (Brookmeyer and Gail, 19861. For the short term, the projections produced by this method are similar to those made by CDC. Near the time that this update is published, a second PHS conference will be held to revise short- and long-term projections of HIV seropre- valence and AIDS cases and deaths. 57

58 CONFRONTING AIDS: UPDATE 1988 THE USES OF MODELS Mathematical models are used to project the prevalence and inci- dence of HIV infection and AIDS in specific regions or populations, define the conditions necessary for a given subpopulation to sustain an epidemic, assess the possible consequences of educational interven- tions aimed at modifying sexual behavior and drug abuse, help plan for health care services, clarify what data are required to predict future trends, and interpret existing data (May and Anderson, 1987; Hyman and Stanley, in press). A range of models, from simple to complex, can be constructed. The challenge, however, is to build models that are simple enough to be useful but complex enough to reflect the realities of the disease. Since 1986 there has been some progress in refining the different types of models that have been used and in defining the types of questions to which they are applicable (eager and Ruitenberg, 1987; IOM, 1988~. Forecasts of the future course of the epidemic depend heavily on underlying assumptions about the transmission dynamics of HIV infec- tion. Modeling these dynamics is extremely difficult because of the large number of biological and behavioral variables required to describe the spread of HIV infection. For example, assumptions must be made about the length of time from a person's initial infection to the manifestation of disease and the relative infectiousness of a person during this time, assumptions that are less important in diseases such as gonorrhea and syphilis, which have shorter incubation periods. In addition, modeling is complicated by the knowledge that the epidemic's characteristics have changed and will continue to change over time with modifications in personal and cultural behaviors and progress in prolonging life and reducing infectivity with antiviral therapy (Hymen and Stanley, in press). One important attribute of models is that they help clarify the assump- tions that underlie different projections and the extent to which the assumptions are sensitive to different parameters (May and Anderson, 1987; De Gruttola and Mayer, 1988~. Modeling the AIDS epidemic is a dynamic process; as additional data on biological and behavioral factors are collected, these assumptions can be refined and the projections improved. Models can produce widely differing projections of the future number of AIDS cases while accurately describing the present picture of the epidemic (De Gruttola and Mayer, 1988~. Yet currently available knowl- edge does not enable researchers to determine which projections are correct. The limitations of the data that are used in models greatly influence their capacity to predict the future accurately.

UNDERSTANDING THE EPIDEMIC'S COURSE 59 DATA NEEDS Current projections about AIDS rely heavily on a limited number of data sets, some of which may be unreliable. (For example, data collected in the 1940s is routinely used to estimate the size of the homosexual population.) The following kinds of information have been identified by modelers as essential for improving the reliability of projections. · Better information is needed about HIV seroprevalence in particular risk groups. Information on the seroprevalence of HIV in defined groups coupled with a knowledge of disease progression will allow more reliable projections of the future number of AIDS cases than will reliance on the reported numbers of cases. · Facts are needed about sexual behavior: the number of sexual partners people have, the partner selection process, the duration of partnership, the frequency and nature of sexual contacts, and the numbers of people involved in high-risk sexual behavior. Available data on sexual behavior in certain segments of the population may be sufficiently flawed that accurate qualita- tive data may serve modelers better than quantitative data obtained from selected nonrepresentative samples (IOM, 19881. · More data are required on the size of the IV drug-abusing population, patterns of sharing needles and syringes among drug abusers, and the interactions between drug abuse and sexual behavior. · Projections made by models are highly sensitive to certain assump- tions about HIV transmission and disease progression. For example, information is needed about the effect of such cofactors as other infec- tions, the variability of infectiousness in an infected individual over the course of infection, the factors that precipitate the transition from asymptomatic to symptomatic states, and the percentage of people who will progress from HIV infection to AIDS. The paucity of information on the behavioral factors influencing the spread of HIV infection has led the Commission on Behavioral and Social Sciences and Education (CBASSE) of the National Research Council to establish the Committee on AIDS Research and the Behavioral, Social, and Statistical Sciences. The committee has been charged to identify ways in which social science expertise can help curb the epidemic. Its work will include an assessment of the adequacy of available data on the scope of the epidemic, the distribution within the population of behavior that sustains it, and an examination of educational efforts to prevent the spread of HIV infection in various subpopulations. The committee's first report is scheduled for release in the fall of 1988. Accurate forecasting of the AIDS epidemic depends in large measure on better knowledge of human sexual and IV drug-abusing behavior.

60 CONFRONTING AIDS: UPDATE 1988 Research in the social sciences, particularly in understanding such behavior, has been inadequate in the past. The committee expects that the CBASSE study will report in detail on the data needs in these areas, propose improved data collection methods, and clarify what is known about the number of people who engage in behavior that spreads HIV infection. The committee adds its endorsement to the importance of the social and behavioral studies needed to understand the epidemic. The committee strongly supports continued research efforts to develop better ways to refine predictions about the future course of the AIDS epidemic and to evaluate potential intervention strategies. To this end, IOM/NAS plans to conduct a series of workshops on modeling over the next 2 years. Modelers, data collectors, and policymakers should meet regularly to ensure that modelers are asking questions for which data can be collected and that data collection is proceeding along lines that will yield information useful to modelers. The questions asked must also be relevant for policymakers. It is important to keep modeling efforts in perspective. Their impor- tance lies in the way projections can help mold prevention strategies and plan care for the sick. Although exact numbers may be important for such purposes as determining the number of hospital beds needed for AIDS patients, trends may be equally useful for targeting interventions. It is likely that the number of AIDS cases will continue to grow for many years, probably into the next century—a prediction based on estimates of the number of individuals currently infected, the projected increase in the distribution of the incubation period, and the proportion of infected individuals who will eventually develop AIDS. Strategies aimed at changing behavior to prevent transmission of the virus thus remain the first priority in the effort to control HIV infection. REFERENCES Brookmeyer, R., and M. H. Gail. 1986. Minimum size of the acquired immunodeficiency syndrome (AIDS) epidemic in the United States. Lancet 2:1320-1322. De Gruttola, V., and K. H. Mayer. 1988. Assessing and modeling heterosexual spread of the human immunodeficiency virus in the United States. Rev. Infect. Dis. 10:138-150. Hyman, J. M., and E. A. Stanley. In press. Using mathematical models to understand the AIDS epidemic. Mathematical Biosciences. IOM (Institute of Medicine). 1988. Report of the Workshop on Modeling the Spread of Infection with Human Immunodeficiency Virus and the Demographic Impact of Acquired Immune Deficiency Syndrome, Washington, D.C., October 15-17, 1987. Jager, H. J. C., and E. J. Ruitenberg. 1987. The statistical analysis and mathematical modelling of AIDS. AIDS 1: 129-130. May, R. M., and R. M. Anderson. 1987. Transmission dynamics of HIV infection. Nature 326: 137-142. Morgan, W. M., and J. W. Curran. 1986. Acquired immunodeficiency syndrome: Current and future trends. Public Health Rep. 101:459-465

Next: 4. Altering the Course of the Epidemic »
Confronting AIDS: Update 1988 Get This Book
 Confronting AIDS: Update 1988
Buy Paperback | $54.95
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

How far have we come in the fight against AIDS since the Institute of Medicine released Confronting AIDS: Directions for Public Health, Health Care, and Research in 1986? This updated volume examines our progress in implementing the recommendations set forth in the first book. It also highlights new information and events that have given rise to the need for new directions in responding to this disease.


  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook,'s online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!