An in-silico game theoretic approach for health intervention efficacy assessment

Abstract

The global rise of multi-strain epidemics has raised significant concerns in the field of public health. To address this, our research introduces a game-theoretic approach to predict the evolutionary dynamics of multi-strained pathogens. Our proposed model sheds light on the pivotal role of vaccination in controlling the growth of such infectious diseases. Here, we propose a modified Susceptible-Vaccinated-Infected-Recovered (SVIR) model featuring two strains and corresponding vaccines: one is the primary vaccine that is designed to target the original strain (effectiveness: e1) and simultaneously exhibits some effectiveness against the mutant strain (e2), another is the mutant vaccine that concentrates on the mutant strain (η2) while showing significant effectiveness against the primary strain (η1). Next, we present a comprehensive time series analysis to examine the fraction of the vaccinated population who adopted these two vaccines. This work elucidates that with a slight increase effectiveness- setting e1=0.5, e2=0.3, η1=0.6, and η2=0.7- the mutant vaccine works more proficiently under both imitation dynamics known as Individual-Based Risk Assessment (IB-RA) and Strategy-Based Risk Assessment (SB-RA). Furthermore, a detailed analysis comparing these two imitation dynamics is demonstrated and also to reconcile the matter that the Strategy-Based-Risk-Assessment process should be adopted to minimize epidemic size. Finally, considering individuals’ attitudes and behaviors towards vaccination, we introduce a replicator equation. Subsequently, a thorough examination of the relationship between imitation dynamics and behavioral dynamics is presented where imitation dynamics outstripped behavioral dynamics which is confirmed by the use of heat maps.