The effect of opinion clustering on disease outbreaks

Abstract

Many high-income countries currently experience large outbreaks of vaccine-preventable diseases such as measles despite the availability of highly effective vaccines. This phenomenon lacks an explanation in countries where vaccination rates are rising on an already high level. Here, we build on the growing evidence that belief systems, rather than access to vaccines, are the primary barrier to vaccination in highincome countries, and show how a simple opinion formation process can lead to clusters of unvaccinated individuals, leading to a dramatic increase in disease outbreak probability. In particular, the effect of clustering on outbreak probabilities is strongest when the vaccination coverage is close to the level required to provide herd immunity under the assumption of random mixing. Our results based on computer simulations suggest that the current estimates of vaccination coverage necessary to avoid outbreaks of vaccine-preventable diseases might be too low.

Statistical physics of vaccination

“…As in the case of [434,574,580,583], when opinions of individuals towards vaccination are influenced by their neighbors or social groups on networks, the susceptible/unvaccinated clusters can readily arise, which directly affects the likelihood of disease outbreak. Even with very high vaccination coverage, outbreaks can still occur via these clusters [434,574].…”

“…For further details on the role of vaccination sentiment in realistic networks, we also refer to Sections 10 and 11 for more details. The aforementioned studies have proved that positively assortative non-vaccinated individuals in contact networks can lead to an enlarged likelihood of large disease outbreaks [434,574,580]. To extend the universality of this finding, Barclay et al investigated whether such a pattern existed in influenza vaccination of contact networks among high schoolers [583].…”

“…Besides payoff driven updating, opinion dynamics is another possible explanation for the large outbreaks of some vaccine-preventable diseases such as measles in many high-vaccination countries [578,579]. Recently, Salathé et al assigned either a positive or a negative opinion about vaccination to each node [574], where positive opinion means that the agent will take up vaccination immediately, while individual with negative opinion reject vaccination due to personal beliefs, such as the safety and efficiency of vaccine, religious beliefs, and related considerations. Subsequently, an opinion formation process occurs based on the dissimilarity index and the strength of opinion formation.…”

“…Although vaccination levels increase in some regions, clusters of non-vaccinators (susceptible individuals) exacerbate transmission and elevate the probability of an outbreak. In this regard, imitation behavior may shed new light into the high incidence of measles in many countries with high vaccine uptake [243,[574][575][576][577]. Realistic policies should thus consider the impact of non-vaccinated clusters, and empirical data on vaccination behavior should be explored also in terms of geolocating potential hotspots of non-vaccinated individuals by public health authorities.…”

Modelling behavioural contagion

“…For example, if g m ¼ g and t m ¼ mt, we recover the SIS dynamics of Funk et al [1] and Kiss et al [7]. As another example, the approach of Salathé & Bonhoeffer [6] takes t m ¼ m/n, g m ¼ (n 2 m)/n. Importantly, both of these schemes depend only on the mean of D m and so are unaffected by the different distributions proposed later.…”