Sudden transitions in coupled opinion and epidemic dynamics with vaccination
Authored by Nuno Crokidakis, Marcelo A Pires, Andre L Oestereich
Date Published: 2018
DOI: 10.1088/1742-5468/aabfc6
Sponsors:
Brazilian Ministry of Education (CAPES)
Brazilian National Council for Scientific and Technological Development (CNPq)
Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
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Abstract
This work consists of an epidemic model with vaccination coupled with an
opinion dynamics. Our objective was to study how disease risk perception
can influence opinions about vaccination and therefore the spreading of
the disease. Differently from previous works we have considered
continuous opinions. The epidemic spreading is governed by an SIS-like
model with an extra vaccinated state. In our model individuals vaccinate
with a probability proportional to their opinions. The opinions change
due to peer influence in pairwise interactions. The epidemic feedback to
the opinion dynamics acts as an external field increasing the
vaccination probability. We performed Monte Carlo simulations in
fully-connected populations. Interestingly we observed the emergence of
a first-order phase transition, besides the usual active-absorbing phase
transition presented in the SIS model. Our simulations also show that
with a certain combination of parameters, an increment in the initial
fraction of the population that is pro-vaccine has a twofold effect: it
can lead to smaller epidemic outbreaks in the short term, but it also
contributes to the survival of the chain of infections in the long term.
Our results also suggest that it is possible that more effective
vaccines can decrease the long-term vaccine coverage. This is a
counterintuitive outcome, but it is in line with empirical observations
that vaccines can become a victim of their own success.
Tags
Agent-based models
Complex networks
behavior
Population dynamics
critical phenomena of socio-economic systems
networks
Model
vaccines
disease
epidemic modelling
Outbreaks
Pandemic influenza
Spread
Endemic states