Understanding the effects of different HIV transmission models in individual-based microsimulation of HIV epidemic dynamics in people who inject drugs

Abstract

We investigated how different models of HIV transmission, and assumptions regarding the distribution of unprotected sex and syringe-sharing events ('risk acts'), affect quantitative understanding of HIV transmission process in people who inject drugs (PWID). The individual-based model simulated HIV transmission in a dynamic sexual and injecting network representing New York City. We constructed four HIV transmission models: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load individual assigned; and model 4, two groups of partnerships (low and high risk). Overall, models with less heterogeneity were more sensitive to changes in numbers risk acts, producing HIV incidence up to four times higher than that empirically observed. Although all models overestimated HIV incidence, micro-simulations with greater heterogeneity in the HIV transmission modelling process produced more robust results and better reproduced empirical epidemic dynamics.

Keywords: Condom use; HIV; Monte Carlo simulation; individual-based model.

Fig. 1.

(a) Estimated HIV prevalence and (b) annualized incidence in people who inject drugs (PWID) in New York, from 1992 to 2002, obtained from a Monte Carlo simulation of an individual-based model, considering four models for HIV transmission probability. In each panel, four HIV transmission probability models for sexual and parenteral transmission are presented: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load is individual assigned; model 4, two groups of partnerships (low and high risk). Red, dotted line indicates the empirical estimates of HIV (a) prevalence [27], and (b) incidence [142] observed in New York in PWID, from 1992 to 2002. In panel (b) the grey area represents the ‘burn-in’ period, since HIV incidence was annualized, and there are no estimated data before 1993. ART, Antiretroviral therapy.

Fig. 2.

Relative bias in (a) HIV prevalence and (b) annualized HIV incidence in people who inject drugs (PWID) in New York, in 2002, obtained from a Monte Carlo simulation of the individual-based model, considering four models for HIV transmission probability. In each panel are represented estimates considering four HIV transmission probability models for sexual and parenteral infections: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load is individual assigned; model 4, two groups of partnerships (low and high risk). The percentage relative biases were calculated relative to the observed HIV prevalence and incidence observed in 2002, respectively to panel (a) and (b) HIV prevalence [27, 142].

Fig. 3.

Projected HIV incidence (per 100 person-years) in 2002 in people who inject drugs (PWID) in New York, considering four models for HIV transmission probabilities, for three different sensitivity analyses: (a) changes in number of risk acts, (b) per-act transmission probability, and (c) number of acts and per-act probabilities. In (a–c) for each HIV transmission probability model (1–4), we considered four scenarios for the number of unprotected sex acts and needle and syringe sharing, respectively, and the per-act transmission probabilities, where the baseline parameter values are increased by 25% and 50%, and decreased by 25% and 50%. In each panel, four HIV transmission probability models are presented: model 1, constant probabilities; model 2, random number of sexual and parenteral acts; model 3, viral load is individual assigned; model 4, two groups of partnerships (low and high risk). Red-dotted line indicates the highest HIV incidence (in log scale) observed in New York in PWID, from 1992 to 2002, as estimated in Des Jarlais et al. [142].