Martí-Franquès COFUND Fellowship Programme


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Reference:

2018MFP-COFUND-27

Area:

Engineering and Architecture

Supervisor name and surname:

Alex Arenas

Supervisor email:

alexandre.arenas@urv.cat

Supervisor short biography

PhD programme:

Computer Science and Mathematics of Security

Title of the research project:

Epidemics and vaccination dilemma in complex networks

Description of the research project:

The quantitative study of diseases propagation has captured the attention of statistical physicists since long. Specifically, this approach has shed light on many conundrums by considering the networked structure of contacts, their time-varying and multi-layer character, as well as the recurrent nature of mobility patterns. Vaccination, whenever possible, is the most effective way to harness and prevent the spreading of a disease. Under normal circumstances, the decision of getting vaccinated can be considered as an act of emph{cooperation}, since it bestows benefits on the whole population at the expenses of single individuals. Notwithstanding, we are recently witnessing the emergence of widespread anti-vaccine movements, which are mainly fueled by misconceptions and mischievous news about vaccines. Scientists (including physicists) are devoting tremendous efforts in designing efficient immunization strategies as well as shedding light on the mechanisms behind the deliberate decision of not getting vaccinated and their harmful consequences .
Vaccination can be modeled as a strategy of a game. Under such premises, the evolution of vaccines` voluntary adoption can be investigated using the machinery of game theory and statistical physics. The first studies carried out on vaccination games use classical game theory, with single round games in which agents have perfect knowledge of their odds to get infected. In reality, however, individuals are not perfectly aware of the risk to get infected, and vaccination coverage may evolve in time as byproduct of personal experiences or imitation. Therefore, evolutionary game theory is the natural workbench to tackle the problem. The seminal works in this direction assumed the simultaneous evolution of vaccination and spreading dynamics. A different approach was used in the case of seasonal influenza, by considering that the spreading process reaches its stationary state before vaccination games take place. We aim at introducing a mean-field framework to mimic the spontaneous adoption of vaccines against influenza-like diseases. The idea is to capture the essence of previous approaches to gauge, analytically, the risk of epidemic outbreaks. In particular, using a minimal evolutionary vaccination game we will infer the strategies adopted before an epidemic season. This, in turns, will allow us to estimate the risk of future outbreaks by encapsulating agents` strategies into an epidemic model. The thesis aims at unveiling how the interplay between the probability of infection, vaccine effectiveness, and cost, gives rise to non-linear responses in vaccine uptake.

Gross anual salary:

26443.80 €

Dedication:

Full time

Working hours:

37.5 hours a week

Expected start date:

European union This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713679