Searched for: author%3A%22Prasse%2C+B.%22
(1 - 15 of 15)
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Persoons, R.D.L. (author), Sensi, M. (author), Prasse, B. (author), Van Mieghem, P.F.A. (author)
We extend the N-intertwined mean-field approximation (NIMFA) for the susceptible-infectious-susceptible (SIS) epidemiological process to time-varying networks. Processes on time-varying networks are often analyzed under the assumption that the process and network evolution happen on different timescales. This approximation is called timescale...
journal article 2024
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Prasse, B. (author), Achterberg, M.A. (author), Van Mieghem, P.F.A. (author)
During the outbreak of a virus, perhaps the greatest concern is the future evolution of the epidemic: How many people will be infected and which regions will be affected the most? The accurate prediction of an epidemic enables targeted disease countermeasures (e.g., allocating medical staff and quarantining). But when can we trust the...
journal article 2022
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Prasse, B. (author), Van Mieghem, P.F.A. (author)
A network consists of two interdependent parts: the network topology or graph, consisting of the links between nodes and the network dynamics, specified by some governing equations. A crucial challenge is the prediction of dynamics on networks, such as forecasting the spread of an infectious disease on a human contact network. Unfortunately,...
journal article 2022
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Tewarie, Prejaas (author), Prasse, B. (author), Meier, Jil (author), Mandke, Kanad (author), Warrington, Shaun (author), Stam, Cornelis J (author), Brookes, Matthew J. (author), Van Mieghem, P.F.A. (author), Sotiropoulos, Stamatios N. (author), Hillebrand, Arjan (author)
How temporal modulations in functional interactions are shaped by the underlying anatomical connections remains an open question. Here, we analyse the role of structural eigenmodes, in the formation and dissolution of temporally evolving functional brain networks using resting-state magnetoencephalography and diffusion magnetic resonance...
journal article 2022
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Achterberg, M.A. (author), Prasse, B. (author), Ma, L. (author), Trajanovski, S. (author), Kitsak, M.A. (author), Van Mieghem, P.F.A. (author)
Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based...
journal article 2022
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Achterberg, M.A. (author), Prasse, B. (author), Van Mieghem, P.F.A. (author)
We analyze continuous-time Markovian ϵ-SIS epidemics with self-infections on the complete graph. The majority of the graphs are analytically intractable, but many physical features of the ϵ-SIS process observed in the complete graph can occur in any other graph. In this work, we illustrate that the timescales of the ϵ-SIS process are related...
journal article 2022
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Prasse, B. (author)
The field of epidemiology encompasses a broad class of spreading phenomena, ranging from the seasonal influenza and the dissemination of fake news on online social media to the spread of neural activity over a synaptic network. The propagation of viruses, fake news and neural activity relies on the contact between individuals, social media...
doctoral thesis 2021
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Tewarie, Prejaas (author), Prasse, B. (author), Meier, Jil (author), Byrne, Áine (author), Di Domenico, Manlio (author), Stam, Cornelis J (author), Brookes, M.J. (author), Hillebrand, Arjan (author), Daffertshofer, Andreas (author), Coombes, Stephen (author), Van Mieghem, P.F.A. (author)
Large-scale neurophysiological networks are often reconstructed from band-pass filtered time series derived from magnetoencephalography (MEG) data. Common practice is to reconstruct these networks separately for different frequency bands and to treat them independently. Recent evidence suggests that this separation may be inadequate, as there...
journal article 2021
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Prasse, B. (author), Devriendt, K.L.T. (author), Van Mieghem, P.F.A. (author)
Infectious diseases typically spread over a contact network with millions of individuals, whose sheer size is a tremendous challenge to analyzing and controlling an epidemic outbreak. For some contact networks, it is possible to group individuals into clusters. A high-level description of the epidemic between a few clusters is considerably...
journal article 2021
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Pizzuti, Clara (author), Socievole, Annalisa (author), Prasse, B. (author), Van Mieghem, P.F.A. (author)
Initially emerged in the Chinese city Wuhan and subsequently spread almost worldwide causing a pandemic, the SARS-CoV-2 virus follows reasonably well the Susceptible–Infectious–Recovered (SIR) epidemic model on contact networks in the Chinese case. In this paper, we investigate the prediction accuracy of the SIR model on networks also for...
journal article 2020
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Prasse, B. (author), Achterberg, M.A. (author), Ma, L. (author), Van Mieghem, P.F.A. (author)
At the moment of writing, the future evolution of the COVID-19 epidemic is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the COVID-19 epidemic in the Hubei province. The network is composed of the cities in Hubei and their...
journal article 2020
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Tewarie, Prejaas (author), Prasse, B. (author), Meier, J.M. (author), Santos, Fernando A.N. (author), Douw, Linda (author), Schoonheim, Menno M. (author), Stam, Cornelis J. (author), Van Mieghem, P.F.A. (author), Hillebrand, Arjan (author)
Functional brain networks are shaped and constrained by the underlying structural network. However, functional networks are not merely a one-to-one reflection of the structural network. Several theories have been put forward to understand the relationship between structural and functional networks. However, it remains unclear how these...
journal article 2020
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Prasse, B. (author), Van Mieghem, P.F.A. (author)
The majority of epidemic models are described by non-linear differential equations which do not have a closed-form solution. Due to the absence of a closed-form solution, the understanding of the precise dynamics of a virus is rather limited. We solve the differential equations of the N-intertwined mean-field approximation of the susceptible...
journal article 2020
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Prasse, B. (author), Van Mieghem, P.F.A. (author)
The majority of research on epidemics relies on models which are formulated in continuous-time. However, processing real-world epidemic data and simulating epidemics is done digitally and the continuous-time epidemic models are usually approximated by discrete-time models. In general, there is no guarantee that properties of continuous-time...
journal article 2019
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Prasse, B. (author), Van Mieghem, P.F.A. (author)
The SIS dynamics of the spread of a virus crucially depend on both the network topology and the spreading parameters. Since neither the topology nor the spreading parameters are known for the majority of applications, they have to be inferred from observations of the viral spread. We propose an inference method for both topology and spreading...
journal article 2019
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