Which is more effective for suppressing an infectious disease: imperfect vaccination or defense against contagion?

Abstract

We consider two imperfect ways to protect against an infectious disease such as influenza, namely vaccination giving only partial immunity and a defense against contagion such as wearing a mask. We build up a new analytic framework considering those two cases instead of perfect vaccination, conventionally assumed as a premise, with the assumption of an infinite and well-mixed population. Our framework also considers three different strategy-updating rules based on evolutionary game theory: conventional pairwise comparison with one randomly selected agent, another concept of pairwise comparison referring to a social average, and direct alternative selection not depending on the usual copying concept. We successfully obtain a phase diagram in which vaccination coverage at equilibrium can be compared when assuming the model of either imperfect vaccination or a defense against contagion. The obtained phase diagram reveals that a defense against contagion is marginally inferior to an imperfect vaccination as long as the same coefficient value is used. Highlights – We build a new analytical framework for a vaccination game combined with the susceptible-infected-recovered (SIR) model. – Our model can evaluate imperfect provisions such as vaccination giving only partial immunity and a defense against contagion. – We obtain a phase diagram with which to compare the quantitative effects of partial vaccination and a defense against contagion.

Modelling and analysing the coexistence of dual dilemmas in the proactive vaccination game and retroactive treatment game in epidemic viral dynamics

“…We now examine the simulation results of equations (2.1)–(2.8), and hence find the critical P of the maximal potential antiviral treatment beyond which scriptRr is not going beyond scriptRs. Here, the proportion of vaccinated individuals is assumed constant ( x = 0.5) [5,6,53]. The experimental results in the left panels of figure 2 are summarized below: Panel (a-i): When the probability of treatment acceptance ω was low (0.05), the antiviral treatment did not effectively eradicate the disease and the sensitive strain always dominated the resistant strain.Panel (a-ii): When ω increased to 0.1, we observed a critical intersection point (scriptPc) at which the control reproduction number of the sensitive strain equalled that of the resistant strain.Panel (a-iii): When the probability of treatment acceptance exceeded the probability of treatment refusal ( ω = 0.6), the control reproduction number of both strains could be less than 1, indicating that treatment could fully eradicate the disease.…”

“…To elucidate the mechanism of infectious-disease control, these approaches incorporate a two-layer time scale: a local time scale (epidemic season) of epidemic diffusion and a global time scale on which the strategy updates at the end of the season (at local equilibrium), followed by repeated seasons. Kuga & Tanimoto [53] developed a theoretical model of imperfect vaccination on local and global time scales and validated it by MAS. However, Kabir & Tanimoto [54] claimed that an individual's decision to take a vaccination after social learning (dynamical behaviour) also occurs on local time scales, so this strategy should be updated instantly.…”

Cost-efficiency analysis of voluntary vaccination against n-serovar diseases using antibody-dependent enhancement: A game approach

“…More recently, a number of precursors used theoretical models to better understand epidemic dynamics in both local (single epidemic season) and global (repeated seasons) time scales, in order to explore how to control contagious diseases. Among them, notable studies include for indicating a meta-population migration model, Kuga and Tanimoto (2018) for the imperfectness of vaccines using both a multi-agent simulation and theoretical approach, Tanaka and Tanimoto (2020) for presenting a subsidy model, Kabir et al (2020), for the vaccination game approach with heterogeneous network and buzz effect, and Alam et al's (Kabir et al, 2019c) vaccination game model for introducing the secondary effect of a vaccine in repeated seasons. Additionally, established a mathematical model of SVIR at a local time scale by allowing for the effectiveness of imperfect vaccination, whereas, Bauch and Bhattacharyya (2012) and others (Chen and Fu, 2018;Bauch, 2005) succeeded in establishing local time evolutionary epidemic models based on the social learning approach.…”

Heterogeneous update mechanisms in evolutionary games: Mixing innovative and imitative dynamics

“…[12,14,[16][17][18]), the application of innovative and non-innovative updat-ing rules leads to results that can be drastically different. As a very interesting and recent example, [19] showed how innovative strategies towards vaccination can lead to different dynamics than the usual imitative ones, changing the vaccination coverage. It is interesting to observe that imitative mechanisms are usually associated with long term biological evolution.…”