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DYNAMOD-VACCINE-DATA

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A new method for dynamic opinion modelling of surveys applied to vaccine hesitan...

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

31/05/2023

UNIVERSITY OF LIME...

295K€

Presupuesto del proyecto: 295K€

Líder del proyecto

UNIVERSITY OF LIMERICK No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-05-31

MSCA-IF-2019: Individual Fellowships Objective:The goal of the Individual Fellowships is to enhance the creative and innovative potential...

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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1 Participantes

UNIVERSITY OF LIMERICK

294.89K€ | Lider

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Duración del proyecto: 38 meses Fecha Inicio: 2020-03-05
Fecha Fin: 2023-05-31

Líder del proyecto

UNIVERSITY OF LIMERICK No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 295K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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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