Agent-Based Modeling Of Infectious Disease Dynamics: Insights into Tuberculosis, Pediatric HIV, and Tuberculosis-HIV Coinfection

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), and human immunodeficiency virus-1 (HIV) are major public health concerns, individually and in combination. The status of the host immune system, previous Mtb infection and HIV-mediated T cell exhaustion, can have significant impacts on immune dynamics during reinfection. Individuals with asymptomatic latent TB infection (LTBI) may be protected against Mtb reinfection, as demonstrated by animal and in vitro studies. However, the underlying dynamics and protective mechanisms of LTBI are poorly understood. In HIV, long-term infection in children and associated T cell exhaustion leads to weakened immune responses to HIV reinfection. The complexity of these infections, particularly in the context of the heightened vulnerability of HIV+ individuals to TB, underscores the need for novel investigative approaches to study host-pathogen and pathogen-pathogen interactions. To this, we have developed an agent-based model (ABM) as a mechanistic computational tool to simulate the immune response to Mtb and HIV, separately and during coinfection. Our ABM integrates clinical and experimental data; simulates immune cell dynamics between macrophages, CD4+ and CD8+ T cells; and produces emergent granuloma-like structures – a critical response to Mtb. This in silico approach allows us to efficiently explore host-pathogen interactions and their clinical implications. By unraveling the complex interplay of immune cell activation, T cell exhaustion, and pathogen dynamics, our model offers insights that could guide the development of targeted therapies. By quantifying the multifaceted nature of these diseases and their interactions, we highlight the potential of computational approaches in understanding and treating complex diseases, individually and in combination.