Over the past two decades, mathematical and computational models have increasingly been used to inform epidemic response. Among these approaches, individual-based models (IBMs) offer a mechanistic framework in which population-level outcomes emerge from simulated interactions between individual agents characterized by demographic and behavioral attributes. Their capacity to represent contact heterogeneity and fine-grained interventions has made IBMs particularly valuable in assessing the effectiveness of interventions and policies aimed at controlling the spread of the COVID-19 pandemic and other infectious disease outbreaks. Although IBMs proliferated rapidly, a systematic understanding of when and where these models were applied, the contexts they addressed, and the methodological approaches favored remains limited. Here, we present a systematic review of 886 IBM studies developed for viral respiratory pathogens between January 1, 2020, and December 31, 2024. Our analysis shows that publication frequency peaked in late 2021, with a geographical distribution that shows a strong positive correlation with national GDP (Pearson's r = 0.93, p < 0.0001), leaving regions such as Africa and South America significantly understudied. Most studies focused on SARS-CoV-2 and aimed to assess the impact of public health interventions. Research priorities evolved over time, shifting from social distancing early in the pandemic to vaccination in later years. While age was included in 72.9% of studies, other sociodemographic factors such as race/ethnicity were rarely considered. This review maps the global landscape of IBM applications, providing a broad overview of the field evolution. Our findings highlight key geographic and sociodemographic trends and gaps, which can be instrumental to guide future modeling efforts for epidemic/pandemic preparedness.
