  Cost-effectiveness analyses, based on decision-analytic models of disease
progression and treatment, are routinely used to assess the economic value of a
new intervention and consequently inform reimbursement decisions for the
intervention. Many decision-analytic models developed to assess the economic
value of highly effective directly acting antiviral (DAA) treatments for the
hepatitis C virus (HCV) infection do not incorporate the transmission dynamics
of HCV, accounting for which is required to estimate the number of downstream
infections prevented by curing an infection. In this study, we develop and
validate a comprehensive agent-based simulation (ABS) model of HCV transmission
dynamics in the Indian context and use it to: (a) quantify the extent to which
the cost-effectiveness of a DAA is underestimated - as a function of its uptake
rate - if disease transmission dynamics are not considered in a
cost-effectiveness analysis model; and (b) quantify the impact of the frequency
and timing of treatment with DAAs, also as a function of their uptake rate,
within a disease surveillance period on its cost-effectiveness. The process of
accomplishing the above research objectives also motivated the development of a
novel random sampling and allocation based approach, along with associated
theoretical grounding, to estimate individual-level outcomes within an ABS that
incurs substantially lower computational expense than the benchmark incremental
accumulation approach.
