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

Total Cost €


EC-Contrib. €






Project "DYNAMOD-VACCINE-DATA" data sheet

The following table provides information about the project.


Organization address
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contact info
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 Coordinator Country Ireland [IE]
 Total cost 294˙886 €
 EC max contribution 294˙886 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2019
 Funding Scheme MSCA-IF-EF-CAR
 Starting year 2020
 Duration (year-month-day) from 2020-06-01   to  2023-05-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF LIMERICK IE (LIMERICK) coordinator 294˙886.00


 Project 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.

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The information about "DYNAMOD-VACCINE-DATA" are provided by the European Opendata Portal: CORDIS opendata.

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lastchecktime (2022-05-26 21:26:46) correctly updated