Browsing by Author "K. Rizi, Abbas"

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Directed percolation in temporal networks
(2022-05-25) Badie Modiri, Arash; K. Rizi, Abbas; Karsai, Marton; Kivelä, Mikko
Letter
Connectivity and reachability on temporal networks, which can describe the spreading of a disease, the dissemination of information, or the accessibility of a public transport system over time, have been among the main contemporary areas of study in complex systems for the last decade. However, while isotropic percolation theory successfully describes connectivity in static networks, a similar description has not yet been developed for temporal networks. Here, we address this problem and formalize a mapping of the concept of temporal network reachability to percolation theory. We show that the limited-waiting-time reachability, a generic notion of constrained connectivity in temporal networks, displays a directed percolation phase transition in connectivity. Consequently, the critical percolation properties of spreading processes on temporal networks can be estimated by a set of known exponents characterizing the directed percolation universality class. This result is robust across a diverse set of temporal network models with different temporal and topological heterogeneities, while by using our methodology we uncover similar reachability phase transitions in real temporal networks too. These findings open up an avenue to apply theory, concepts, and methodology from the well-developed directed percolation literature to temporal networks.
Effectiveness of contact tracing on networks with cliques
(2024-02-09) K. Rizi, Abbas; Keating, Leah; Gleeson, James P.; O'Sullivan, David; Kivelä, Mikko
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here we show that this strategy is particularly effective in the presence of social groups: Once the disease enters a group, contact tracing not only cuts direct infection paths but can also pre-emptively quarantine group members such that it will cut indirect spreading routes. We show these results by using a deliberately stylized model that allows us to isolate the effect of contact tracing within the clique structure of the network where the contagion is spreading. This will enable us to derive mean-field approximations and epidemic thresholds to demonstrate the efficiency of contact tracing in social networks with small groups. This analysis shows that contact tracing in networks with groups is more efficient the larger the groups are. We show how these results can be understood by approximating the combination of disease spreading and contact tracing with a complex contagion process where every failed infection attempt will lead to a lower infection probability in the following attempts. Our results illustrate how contact tracing in real-world settings can be more efficient than predicted by models that treat the system as fully mixed or the network structure as locally treelike.
Herd immunity and epidemic size in networks with vaccination homophily
(2022-05-12) Hiraoka, Takayuki; K. Rizi, Abbas; Kivelä, Mikko; Saramäki, Jari
Letter
We study how the herd immunity threshold and the expected epidemic size depend on homophily with respect to vaccine adoption. We find that the presence of homophily considerably increases the critical vaccine coverage needed for herd immunity and that strong homophily can push the threshold entirely out of reach. The epidemic size monotonically increases as a function of homophily strength for a perfect vaccine, while it is maximized at a nontrivial level of homophily when the vaccine efficacy is limited. Our results highlight the importance of vaccination homophily in epidemic modeling.
Spreading and Epidemic Interventions - Effects of Network Structure and Dynamics
(2024) K. Rizi, Abbas
School of Science | Doctoral dissertation (article-based)
The COVID-19 pandemic has highlighted the critical importance of understanding epidemic dynamics, particularly the significant gaps in our knowledge that need addressing to better prepare for future pandemics. This thesis delves into the intricacies of disease spread within complex human interaction networks, underlining the pivotal role of individual connectedness in influencing epidemic outcomes. By developing theoretical models inspired by real-world epidemiological data, this work provides a nuanced exploration of disease transmission dynamics across networked populations, emphasizing the heterogeneous, spatial, homophilic, and temporal characteristics inherent in human social structures. A primary focus of this research is the investigation of intervention strategies, encompassing pharmaceutical measures, such as vaccination campaigns, and non-pharmaceutical interventions, including contact tracing techniques. These interventions are evaluated within more realistic network topologies, characterized by degree heterogeneity and group structures, to assess their effectiveness in mitigating epidemic spread. The thesis leverages mathematical and computational epidemiology to offer profound insights into optimizing intervention strategies within the complex web of human interactions, thereby contributing to the academic discourse and providing actionable intelligence for public health policy formulation and epidemic preparedness. The avenues of research opened by this work offer deeper insights into the mechanisms of epidemic spread in social networks. By using stylized modeling, the study was able to delve into the nontrivial ways epidemics spread through social networks. This modeling approach simplified the realworld dynamics into more analytically tractable forms, allowing the researchers to capture the essence of contact network structures and their crucial role in transmitting infectious diseases. The primary objective of this study was to identify new pathways for academic exploration and offer valuable perspectives that can enhance public health policies and epidemic response strategies. Ultimately, this work seeks to contribute to a better understanding of epidemic dynamics by bridging knowledge gaps and fostering a more resilient response to public health challenges in the face of complex human interactions.