Agent-Based Models of Virus Infection

Purpose of ReviewAgent-based modelling (ABM) is a robust computational tool for investigating the dynamics of infectious disease spread and evaluating intervention strategies. This review paper gives an overview of the recent literature on ABM applications in predicting and simulating the spread of infectious diseases in populations.Recent FindingsLatest models incorporate the impact of vaccination rates and intervention strategies. Despite inherent limitations such as data constraints and model simplifications, ABM offers valuable insights into the complex interplay of individual behaviours and population-level outcomes. Understanding these dynamics facilitates evidence-based decision-making in public health, guiding the development of tailored strategies to control infectious disease outbreaks and improve population health outcomes.SummaryThe review highlights developments in the area of ABM, providing an overview of the latest extensions and applications of these models in the field of virus infection. The focus is on how recent advances in computer technology enable more detailed modelling, pushing the boundaries of computational limitations to allow for more detailed simulations. Examples are given to demonstrate how these new insights have impacted the decision-making process.