Modeling the Effect of Observational Social Learning on Parental Decision-Making for Childhood Vaccination and Diseases Spread over Household Networks

TL;DR

This paper introduces a novel Bayesian social learning model on multilayer networks to analyze how observational learning influences parental vaccination decisions and disease spread, highlighting the impact of network topology and social norms.

Contribution

It develops a new Bayesian aggregation model for social learning over multilayer networks, extending existing models like voting and DeGroot, and analyzes its effects on vaccination behavior and disease dynamics.

Findings

Social learning can lead to opinion convergence and increased vaccine uptake.

Network topology significantly influences social learning and disease spread.

Presence of cultural differences affects vaccination norms and coverage.

Abstract

In this paper, we introduce a new model of parental decision-making concerning vaccines against a childhood disease that spreads over a contact network. We consider a bilayer network composed of two overlapping networks which are either Erd\H{o}s-R\'{e}nyi (random) networks or Barab\'{a}si-Albert networks. The new model uses a Bayesian aggregation rule for observational social learning, occurring over a social network, of which other decision models, like voting and DeGroot models, are special cases. Using our new model, we show how some levels of social learning about vaccination preferences can lead to the convergence of opinions and affect levels of vaccine uptake and so disease spread. In addition, we study the effect of the existence of two cultures of social learning on the establishment of social norms of vaccination and levels of vaccine uptake. In all cases, the mutual influence between the dynamics of observational social learning and disease spread is dependent on the network's topology and vaccine safety and availability.