The influence of social norms on the dynamics of vaccinating behaviour for paediatric infectious diseases

Abstract

Mathematical models that couple disease dynamics and vaccinating behaviour often assume that the incentive to vaccinate disappears if disease prevalence is zero. Hence, they predict that vaccine refusal should be the rule, and elimination should be difficult or impossible. In reality, countries with non-mandatory vaccination policies have usually been able to maintain elimination or very low incidence of paediatric infectious diseases for long periods of time. Here, we show that including injunctive social norms can reconcile such behaviour-incidence models to observations. Adding social norms to a coupled behaviour-incidence model enables the model to better explain pertussis vaccine uptake and disease dynamics in the UK from 1967 to 2010, in both the vaccine-scare years and the years of high vaccine coverage. The model also illustrates how a vaccine scare can perpetuate suboptimal vaccine coverage long after perceived risk has returned to baseline, pre-vaccine-scare levels. However, at other model parameter values, social norms can perpetuate depressed vaccine coverage during a vaccine scare well beyond the time when the population's baseline vaccine risk perception returns to pre-scare levels. Social norms can strongly suppress vaccine uptake despite frequent outbreaks, as observed in some small communities. Significant portions of the parameter space also exhibit bistability, meaning long-term outcomes depend on the initial conditions. Depending on the context, social norms can either support or hinder immunization goals.

Statistical physics of vaccination

“…As such, a first line of inquiry should use mean-field models for investigating the implications of further social, economic, or psychological theories of behavior relevant for vaccination in order to achieve a more detailed understanding of the "typically observed" behaviour (see e.g. [400]). A second line should aim to more extensively develop the-just initiated-empirical use of mean-field models by applying them over different datasets (e.g.…”

“…by building selflegitimation for non-vaccinators, by enforcing their cohesion, or by increasing the conformism of vaccinators as a majority. Oraby et al [400] included ISNs into the IGD model of section 8.2.3 by assuming, in line with the game-theoretical work by [470], that ISNs add a new component to payoffs which is proportional to group pressure, i.e. to how many other people in the population are also playing that strategy.…”

“…In particular, ISNs can mitigate the main perverse consequence of prevalence-dependence, namely the "elimination impossible" result, by allowing the pure vaccinator equilibrium to become stable at large levels of social pressure. Other [400] effects are more controversial, as social pressures can induce bistability phenomena in a large portion of the parameter space, with the well-known amplification of uncertainity due to both the imprecise measurement of initial state and and the equilibrium switch that can be induced by extrinsic stochasticity. Thus, depending on the context, social norms can either support or hinder vaccination goals.…”

“…However, at some levels of organization (in particular, the aggregate population level) it is often possible to develop simple models that have some predictive power. For instance, mathematical models incorporating behavior-disease interactions have been fitted to time series data of infectious disease prevalence, and model selection ap-proaches such as Aikaike Information Criterion (AIC) have shown that models that include behavior can explain the data more effectively compared to models that neglect behavior, with little or no parsimony penalty [391,399,400]. Such models have also shown retrospective predictive power [391].…”

Early warning signals of regime shifts in coupled human–environment systems

“…Complex community structure in coupled HESs can create multiple small regime shifts instead of a single large one (31). Coupled behavior-disease dynamics can exhibit regime shifts too, where vaccine coverage suddenly plummets when a critical threshold in perceived vaccine risk is exceeded (32).…”