Modeling the incidence of Acquired Immunodeficiency Syndrome (AIDS) in San Francisco, Los Angeles, and New York

Pickering, John; Wiley, James; Padian, Nancy; Lieb, Loren; Echenberg, Dean F.; Walker, Juliette

doi:10.1016/0270-0255(86)90127-2

Cited by 37 publications

(17 citation statements)

References 24 publications

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“…The AIDS incidence amongst MSM in San Francisco has been estimated by several studies [9,13,25,[27][28][29][30][31] to reach the peak during the early to mid 1990s following trends similar to the observed data (Fig. 3a).…”

Section: Epidemic Projectionsmentioning

confidence: 65%

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Mathematical models for the study of HIV spread and control amongst men who have sex with men

et al. 2011

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For a quarter of century, mathematical models have been used to study the spread and control of HIV amongst men who have sex with men (MSM). We searched MEDLINE and EMBASE databases up to the end of 2010 and reviewed this literature to summarise the methodologies used, key model developments, and the recommended strategies for HIV control amongst MSM. Of 742 studies identified, 127 studies met the inclusion criteria. Most studies employed deterministic modelling methods (80%). Over time we saw an increase in model complexity regarding antiretroviral therapy (ART), and a corresponding decrease in complexity regarding sexual behaviours. Formal estimation of model parameters was carried out in only a small proportion of the studies (22%) while model validation was considered by an even smaller proportion (17%), somewhat reducing confidence in the findings from the studies. Nonetheless, a number of common conclusions emerged, including (1) identification of the importance of assumptions regarding changes in infectivity and sexual contact rates on the impact of ART on HIV incidence, that subsequently led to empirical studies to gather these data, and (2) recommendation that multiple strategies would be required for effective HIV control amongst MSM. The role of mathematical models in studying epidemics is clear, and the lack of formal inference and validation highlights the need for further developments in this area. Improved methodologies for parameter estimation and systematic sensitivity analysis will help generate predictions that more fully express uncertainty, allowing better informed decision making in public health.

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Section: Epidemic Projectionsmentioning

confidence: 65%

“…A quarter of a century has passed since the first mathematical model of the HIV epidemic amongst MSM was introduced [9] in 1986 (Fig. 2).…”

Section: Milestones In Model Developmentmentioning

confidence: 99%

Mathematical models for the study of HIV spread and control amongst men who have sex with men

et al. 2011

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“…15 Second, in a follow-up study of 655 homosexual/bisexual men recruited by advertising from a variety of local settings (bars, bathhouses, and AIDS-related investigations), the average number of different male sexual partners per month declined from 6.3 in November 1982 to 3.9 in May 1984 and the average number of times each subject engaged in anal/genital contact with ejaculation with each partner declined from 1.9 to 0.7 in the same time interval. '6 Findings from a study based on a probability sample, such as the SFMHS, may be extrapolated to the entire population of 23,000 single men, 25 to 55 years of age, living in the study area.…”

Section: -86mentioning

confidence: 99%

The San Francisco Men's Health Study: III. Reduction in human immunodeficiency virus transmission among homosexual/bisexual men, 1982-86.

Winkelstein¹,

Samuel²,

Padian³

et al. 1987

Am J Public Health

250

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“…Areas of biological uncertainty, future data needs, and public health policy implications were discussed. Pickering et al (1986) formulated a model for the spread of HIV and AIDS incidence in the homosexual male population in three large cities. They used a discrete time nonlinear model for the sexual transmission of HIV with several possible courses of progression after infection.…”

Section: Dynamic Modelsmentioning

confidence: 99%

Statistical and Mathematical Approaches in HIV/AIDS Modeling: A Review

Schwager

Castillo‐Chavez

Hethcote

1989

Lecture Notes in Biomathematics

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This paper provides a brief introduction to the field and the literature of statistical and mathematical approaches to HIV I AIDS modeling. The emphasis is on succinct description of the methodology and on discussion of the work of some researchers who were unable to contribute to this volume. This review is not complete; however, it is complemented to a great degree by the partial reviews of the literature provided by several authors in their contributions to this volume. An additional objective of this introductory chapter is to describe to nonspecialists, in general terms, some of the main statistical and mathematical approaches currently used in problems related to AIDS epidemiology. This paper provides a brief introduction to the field and the literature of statistical and mathematical approaches to HIV /AIDS modeling. The emphasis is on succinct description of the methodology and on discussion of the work of some researchers who were unable to contribute to this volume. This review is not complete; however, it is complemented to a great degree by the partial reviews of the literature provided by several authors in their contributions to this volume. An additional objective of this introductory chapter is to describe to nonspecialists, in general terms, some of the main statistical and mathematical approaches currently used in problems related to AIDS epidemiology.

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Modeling the incidence of Acquired Immunodeficiency Syndrome (AIDS) in San Francisco, Los Angeles, and New York

Cited by 37 publications

References 24 publications

Mathematical models for the study of HIV spread and control amongst men who have sex with men

Mathematical models for the study of HIV spread and control amongst men who have sex with men

The San Francisco Men's Health Study: III. Reduction in human immunodeficiency virus transmission among homosexual/bisexual men, 1982-86.

Statistical and Mathematical Approaches in HIV/AIDS Modeling: A Review

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