Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Statistical challenges and opportunities in modelling coupled behaviour-disease dynamics of vaccine refusal
Chris Bauch

Last modified: 2018-05-21


Vaccine refusal has proven to be a persistent foe in efforts to protect populations from infectious diseases.  One hypothesis about its origin posits a coupling between vaccinating behaviour and disease transmission: when infection prevalence is sufficiently low, individuals become complacent and vaccinating becomes less desirable, causing a decline in vaccine coverage and resurgence of the disease.  This dynamic is being explored in a growing number of mathematical models.  Here, I present a differential equation model of coupled behaviour-disease dynamics for vaccine-preventable paediatric infections, and I discuss previous research that has applied various statistical methodologies to parameterize and validate the model.  I will show how methodologies such as model selection analysis and statistical learning, in conjunction with mechanistic modelling, can be used to test for the presence of phenomena related to coupled behaviour-disease dynamics during episodes of vaccine refusal.  These phenomena include social learning and imitation, social norms, criticality, and coupling between vaccinating behaviour and disease prevalence.  Some of these methodologies exploit new data sources such as online social media. I conclude that the study and modelling of vaccine refusal can greatly benefit from using mechanistic models informed by both traditional and state-of-the-art statistical methodologies.


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