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IntroductionPersons living with HIV have a disproportionately higher burden of HPV-related cancers. Causal factors include both behavioral and biological. While pharmaceutical and care support interventions help address biological risk of coinfection, as social conditions are common drivers of behaviors, structural interventions are key part of behavioral interventions. Joint modeling sexually transmitted diseases (STD) can help evaluate optimal intervention combinations for overall disease prevention. While compartmental modeling is sufficient for faster spreading HPV, network modeling is suitable for slower spreading HIV. However, using network modeling for jointly modeling HIV and HPV can generate computational complexities given their vastly varying disease epidemiology and disease burden across sub-population groups.MethodsWe applied a recently developed mixed agent-based compartmental (MAC) simulation technique, which simulates persons with at least one slower spreading disease and their immediate contacts as agents in a network, and all other persons including those with faster spreading diseases in a compartmental model, with an evolving contact network algorithm maintaining the dynamics between the two models. We simulated HIV and HPV in the U.S. among heterosexual female, heterosexual male, and men who have sex with men (men only and men and women) (MSM), sub-populations that mix but have varying HIV burden, and cervical cancer among women. We conducted numerical analyses to evaluate the contribution of behavioral and biological factors to risk of cervical cancer among women with HIV.ResultsThe model outputs for HIV, HPV, and cervical cancer compared well with surveillance estimates. Behavioral factors significantly contributed to risk of HIV-HPV co-infection, and biological factors further exacerbated cancer burden among persons with HIV, with the fraction attributed to each factor sensitive to disease burden.ConclusionsThis work serves as proof-of-concept of the MAC simulation technique for joint modeling related diseases with varying epidemiology in sub-populations with varying disease burden. Future work can expand the model to simulate sexual and care behaviors as functions of social conditions, and further, jointly evaluate behavioral, structural, and pharmaceutical interventions for overall STD prevention. |
