In recent years, agent-based modelling (ABM) has been increasingly used to elucidate complex adaptive systems. An ABM is a structural computational system that consists of a collection of abstract objects (agents) embedded in a virtual environment that interact based on a set of prescribed rules. While traditional approaches such as differential equation-based compartmental models span a vast literature, they often impose restrictive assumptions such as homogeneity and determinism that limit their application to real settings. ABM overcomes these limitations through a bottom-up approach in which macro dynamics emerge from micro level phenomena.
During the past decade, there has been a surge of interest in the use of ABM in human health and disease dynamics. While this is rapidly growing, its application to other relevant areas such as health economics is still in infancy, and frameworks that could systematically apply ABM are still lacking. In this thesis, we develop a general framework for cost-effectiveness analysis in which ABM is designed to project the system dynamics. We argue that ABM improves the empirical reliability of policy-oriented simulation models and that it presents an ideal tool to address the complexity of disease processes, project the impact of interventions and inform their optimal implementation. We use this framework in an epidemiological context to quantify the economic impact of vaccination strategies for prevention of infectious diseases.
We present two case studies for a human-to-human infection transmission (i.e., Haemophilus influenzae) and a vector-borne disease (i.e., Zika). In each case, we detail the construction of ABM and its utilization to conduct Bayesian cost-effectiveness analysis of potential vaccine candidates. In addition to uncovering important characteristics of these diseases in epidemic dynamics, we present their first cost-effectiveness analysis and implications for vaccination strategies in different populations settings
