A comparative cost assessment of coalescing epidemic control strategies in heterogeneous social-contact networks

The outcome of an epidemic is contingent upon the mitigating control strategies deployed by policymakers. The deployment of control strategies is constrained by the cost of economic, social, and technological factors, which often depend on regional conditions. Our model facilitates a comparative assessment of costs between two interconnected subpopulations within a heterogeneous social-contact network, which may deploy either aligned or divergent control strategies, as is frequently the case for neighboring countries. Depending on their respective asymmetric resources, each subpopulation can deploy a contact confinement strategy, a vaccination strategy, or a hybrid of both. The multi-objective goal of minimizing both the epidemic burden and the economic cost from policy implementation and productivity losses is compounded using a scaled scalarization method. Due to the inter-dependence of policy outcomes, the optimal strategy is derived from the Nash equilibrium in a 2-player compounded pay-off matrix of the total cost. Our network-based model implements a probabilistic Susceptible-Exposed-Infectious-Recovered-Dead dynamics. It also includes the effects of ego-network support and waning immunity modulated by re-exposure to viral contaminants (‘SEIRSD’). Robust outcomes of repeated network simulations with COVID-19 parameters are examined. Our method aims to equip policymakers in a negotiation process with a comparative cost matrix for divergent mitigation policies and to identify the optimal Nash-based strategy profile.