Dynamics of the HIV Epidemics among Male Injecting Drug Users Using Agent-Based Modeling

Research Article

Austin J HIV/AIDS Res. 2021; 7(1): 1048.

Dynamics of the HIV Epidemics among Male Injecting Drug Users Using Agent-Based Modeling

Le TT¹, Shojaati N² and Lim HJ¹*

¹Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

²Department of Computer Science, College of Arts & Sciences University of Saskatchewan, Saskatoon, Saskatchewan, Canada

*Corresponding author: Hyun Ja Lim, Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, 104 Clinic Place, Saskatoon, SK, S7N 5E5, Canada

Received: March 17, 2021; Accepted: April 09, 2021; Published: April 16, 2021


Background: Although Injecting Drug Users (IDUs) carry a disproportionate burden of HIV, little is known about the dynamics of the HIV epidemics among IDUs.

Objective: This study aimed to characterize the dynamics of the HIV epidemic among IDUs and the effects of alternative HIV prevention intervention strategies using Agent-Based Modeling (ABM).

Methods: ABM was constructed using key behavioral risks. The HIV/STI Surveillance study was utilized to create datasets for simulation. Different intervention scenarios were simulated and compared.

Results: Lowering needle sharing level among IDUs resulted in the largest reductions in both HIV prevalence and the cumulative number of HIV infections over time in all simulated populations. The majority of the reductions occurred when needle sharing declined from the baseline level to 40% and 30%, respectively.

Conclusion: ABM may well complement traditional epidemiological regression-based analysis in providing important insights into the complex dynamics of the HIV epidemics among IDUs.

Keywords: HIV/AIDS; HIV dynamics; Injecting Drug Users (IDU); Agent-Based Model (ABM)


There were an estimated 15.9 million people who might inject drugs worldwide, with nearly three-quarters of these individuals being from low- and middle-income countries [1,2]. Injecting drug use has made a substantial contribution to the HIV epidemic globally [3]. Although IDUs account for an estimated 0.2-0.5% of the world’s population, they make up approximately 5-10% of people living with HIV globally [4]. In many parts of the world, the direct sharing of needles, syringes, and other injection equipment among IDUs has driven the HIV epidemic [2,4]. People who inject drugs have as 22 times higher the rate of HIV infection as the general population [5]. All regions report high HIV prevalence among IDU population, although the severity varies [2]. The HIV prevalence among IDUs ranged from 5% in Eastern Europe to 28% in Asia [2].

Despite the significant burden of HIV among IDUs, little is known about the dynamics of the HIV epidemics in this population. The majority of existing HIV research and analysis primarily rely on statistical regression-based models [6]. The key motivation for the use of regression-based approach has been the desire to estimate the effect of independent risk factors on a particular outcome, after statistically controlling for individual-level attributes which could simultaneously be related to both the risk factors and the outcome [6]. However, with the efforts to isolate the effect of changing a single factor while controlling for all other factors in the model, regression modeling approach might be ill-equipped to investigate the processes embedded in complex systems characterized by dynamic interactions among heterogeneous individuals, and between individuals and their environment [6]. Regression-based models thus present challenges which are associated with the incapacity of examining the dynamic and often reciprocal processes from which the HIV epidemic emerges [6].

There is increasing recognition that advanced computational modeling, which simulates the real world as it might be in a variety of circumstances, may address some of the challenges faced by traditional statistical regression-based methods [6,7]. Agent Based- Modeling (ABM) is a computational simulation method that defines the behavior of a population of heterogeneous individuals within an environment in which characteristics of the population are driven by the interactions of the individuals with each other and with the environment [8]. Compared with other types of HIV models which are built using mathematical or statistical equations, agent-based approach is advantageous in allowing understandings of heterogeneous individuals’ behaviors and interactions between individuals, representation of the environment with which individuals interact, and a natural correspondence to real life situation that enhances model transparency for stakeholders [7,9]. ABM simulation also allows experimentation with the model by answering ‘what-if’ types of questions such as ‘What if certain quantitative assumptions are altered’? ‘Does the model reflect the real system that it represents’? and ‘How will the model behave after a certain period of time’? [9] By experimentation with the model, ABMs can provide insights into the impact of behavioral feedbacks that may occur during the epidemic [8,9]. They further allow policy makers to understand multi level determinants of HIV infection and to design and assess the effectiveness of HIV intervention policies [10]. Agent-based modeling has increasingly been employed in population health research, such as drug and alcohol health behavioral research, obesity, physical activity, and infectious diseases [11-16]. In recent years, agent-based approach has also been applied in HIV epidemic modeling [7-9,17- 19]. However, to the best of our knowledge, there has not been any research using ABM to simulate HIV future trends among IDUs and to assess the effectiveness of HIV prevention interventions targeting this high-risk population. The aims of this study are (i) to characterize the dynamics of the HIV epidemic among IDUs and (ii) to explore the effects of alternative HIV prevention intervention strategies.

Materials and Methods

In this study, key behavioral data from the ‘2009 HIV/STI Integrated Biological and Behavioral Surveillance’ (IBBS) study in Vietnam was utilized for constructing ABM to examine the relevance of agent-based approach in studying the dynamics of the HIV epidemic among IDUs and to explore the effects of various intervention strategies. The IBBS study is the first systematically community-based surveillance survey in Vietnam. More detailed descriptions of the IBBS study have been published elsewhere [20,21]. Briefly, the study was jointly conducted by the Vietnam Ministry of Health and Family Health International (FHI 360) since 2005 to provide estimates of HIV and Sexually Transmitted Infection (STI) prevalence as well as risk behaviors among HIV high-risk populations, including male IDUs and Female Sex Workers (FSWs) and men who have sex with men.

Model description

Model population: Model population size was set as 100,000 individual agents, taking into account the trade-off between the sizes of the population simulated, the sophistication of the model, and the model run time. An agent was drawn from a common population and changed its state (status) over the course of simulated time in the model, depending on its current risk behaviors (Figure 1). Given the intertwining of the drug injection-related epidemic and the heterosexual epidemic in Vietnam [22,23], relevant data on FSW population and HIV risk behaviors were also incorporated to build the model. To achieve close estimates of the proportion of IDUs and FSWs in the population, the model used assumptions from the Asian Epidemic Model developed by Brown and Peerapatanapokin that male IDUs account for 2.0% of adult male population while FSWs account for 1.0% of adult female population. FSW population is consisted of Street-Based Sex Workers (SSWs) (accounting for 30%) and Venue-Based Sex Workers (VSWs) (accounting for 70% of the overall sex worker population) [24]. The Male/Female (M/F) ratio set in this model closely reflected the actual M/F ratio in which males account for 51% and females account for 49% of the population. The population simulated was an open population with birth rate and death rate integrated into the model to capture population inflow and outflow.