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A new method for dynamic opinion modelling of surveys applied to vaccine hesitancy data

Project description

A method to identify vulnerability to vaccine hesitancy

The World Health Organization (WHO) ranks vaccine hesitancy in the top-10 global threats to health. While the global spread of vaccine hesitancy is a complicated phenomenon, no method predicts which countries are most vulnerable to this hesitancy. It is suggested that opinion dynamics models have great potential to recognise the countries where public opinion tends to be vulnerable or resistant. However, even though many successful models exist, only a few of them rely on empirical data. The EU-funded DYNAMOD-VACCINE-DATA project will establish an innovative method to reconstruct social network information from common data types and to provide a tool that identifies societies keen to adopt vaccine hesitancy opinion.

Objective

Vaccine hesitancy (delaying or refusing of vaccination) has been identified by the World Health Organization as one of the top-ten threats to global health. The spreading of vaccine-hesitancy in society is a complex phenomenon and no method can currently predict which countries will become vulnerable to this threat.

Opinion dynamics models have enormous – as yet unrealised – potential to identify countries where vaccine-hesitant opinions are likely to spread or be resisted. They simulate the evolution of public opinion with computational models in which agents interact based on simple rules, with the goal of precisely modelling the spread of opinions in networks. However, while many successful theoretical models exist, few have been run on empirical data. This is because most models require detailed network information and are therefore not compatible with common data types (i.e. survey data).

In this project, I will develop a novel method for reconstructing social network information from survey responses alone. First, the method will be validated using simulations. Then, it will be applied to secondary vaccine-hesitancy survey datasets to compare the predictive capability of different opinion dynamics models in this context.

This study will provide two main outputs. First, a toolkit to identify societies most vulnerable to vaccine-hesitancy opinion spreading. Second, a method for inferring underlying social networks from survey data. This will have general value for research on any social issue related to opinion-coordination, e.g. climate change; GMOs etc.

This fellowship will transfer my mathematical and computational modelling expertise to my hosts. At the same time, it will provide me with synergistic expertise in social science and network science as a platform for my research career in computational social science.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

(opens in new window) H2020-MSCA-IF-2019

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Coordinator

UNIVERSITY OF LIMERICK
Net EU contribution
€ 294 886,08
Address
NATIONAL TECHNOLOGICAL PARK, PLASSEY
- Limerick
Ireland
Region
Ireland Southern Mid-West
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 294 886,08