Public defence in Computer Science, M.Sc. Abbas K. Rizi

Title of the thesis: Spreading and Epidemic Interventions: Effects of Network Structure and Dynamics

Doctoral student: Karimi Rizi Abbas
Opponent: Dr. Nicola Perra,Queen Mary University of London, United Kingdom
Kustos: Prof. Mikko Kivelä, Aalto University School of Science, Department of Computer Science

Unveiling the role of social networks in shaping epidemic spread and interventions 

Mathematical modeling has significantly improved our understanding of how infectious diseases spread and how interventions can be effective. Over time, these models have become more complex, capturing intricate phenomena related to diseases like HIV, flu, and even computer viruses. With our experiences during global health crises like COVID-19, it has become increasingly important to accurately model transmissions via social networks when planning epidemic responses. Traditional epidemic models often overlook the structured nature of social connections. However, this work aims to bridge this gap by contributing to the literature by incorporating the networked structure of human populations into epidemiological modeling. 

The thesis focuses on network epidemiology, a field that benefits from mathematical and computational modeling to analyze how social structures influence disease transmission and the success of intervention strategies such as vaccination, contact tracing, and social distancing. This work explicitly explores how particular aspects of social network structure can impact disease spread and the effectiveness of public health measures. Additionally, the research introduces a new mathematical framework for studying how any spreading agent spreads across dynamic networks. This framework provides a foundation for exploring various spreading phenomena beyond epidemics, paving the way for further research. 

The insights presented in this thesis are expected to offer valuable clues to individuals interested in modeling and navigating future epidemics. It emphasizes the significance of adopting epidemic models that recognize the intricate relationship between human behavior, social structures, and disease dynamics.

Keywords: Complex Systems, Network Science, Spreading Phenomena, Computational Epidemiology, Digital Epidemiology, Complex Networks, Temporal Networks, Reachability, Phase Transitions, Percolation, Covid-19, Vaccination, Contact Tracing

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information:

Doctoral theses of the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52 

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