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Updated: Feb 10, 2026

Chronic, Acute, and Reactivated HIV Infection in Humanized Immunodeficient Mouse Models
Published on: December 3, 2019
Development and Calibration of a Mathematical Model of Anal Carcinogenesis for High-Risk HIV-Infected Men
Emily A Burger1,2, Michael A Dyer3, Stephen Sy1
1Department of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA.
Objectives:
Men who have sex with men who are living with HIV are at highest risk for anal cancer. Our objective was to use empirical data to develop a comprehensive disease simulation model that reflects the most current understanding of anal carcinogenesis, which is uniquely positioned to evaluate future anal cancer screening strategies and provide insight on the unobservable course of the disease.
Setting:
North America.
Methods:
The individual-based simulation model was calibrated leveraging primary data from empirical studies, such as a longitudinal HIV-positive men who have sex with men cohort study [Human Immunodeficiency and Papilloma Virus Research Group (HIPVIRG); n = 247] and the North American AIDS Cohort Collaboration on Research and Design [(NA-ACCORD); n = 13,146]. We used the model to infer unobservable progression probabilities from high-grade precancer to invasive anal cancer by CD4 nadir and human papillomavirus (HPV) genotype.
Results:
The calibrated model had good correspondence to data on genotype- and age-specific HPV prevalence; genotype frequency in precancer and cancer; and age- and nadir CD4-specific cancer incidence. The model-projected progression probabilities differed substantially by HPV genotype and nadir CD4 status. For example, among individuals with CD4 nadir <200, the median monthly progression probability from a high-grade lesion to invasive cancer was 0.054% (ie, 6.28% 10-year probability) and 0.004% (ie, 0.48% 10-year probability) for men with an HPV-16 infection versus without a detectable HPV infection, respectively.
Conclusions:
We synthesized existing evidence into a state-of-the-art anal cancer disease simulation model that will be used to quantify the tradeoffs of harms and benefits of alternative strategies, understand critical uncertainties, and inform national anal cancer prevention policy.
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