| Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei B Prasse, MA Achterberg, L Ma, P Van Mieghem Applied Network Science 5, 1-11, 2020 | 71 | 2020 |
| Comparing the accuracy of several network-based COVID-19 prediction algorithms MA Achterberg, B Prasse, L Ma, S Trajanovski, M Kitsak, P Van Mieghem International journal of forecasting 38 (2), 489-504, 2022 | 69 | 2022 |
| Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches P Tewarie, B Prasse, JM Meier, FAN Santos, L Douw, M Schoonheim, ... NeuroImage, 116805, 2020 | 49 | 2020 |
| Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations K Sherratt, H Gruson, H Johnson, R Niehus, B Prasse, F Sandmann, ... Elife 12, e81916, 2023 | 41 | 2023 |
| Network Reconstruction and Prediction of Epidemic Outbreaks for General Group-Based Compartmental Epidemic Models B Prasse, P Van Mieghem IEEE Transactions on Network Science and Engineering, 2020 | 41 | 2020 |
| Time-dependent solution of the NIMFA equations around the epidemic threshold B Prasse, P Van Mieghem Journal of mathematical biology 81 (6), 1299-1355, 2020 | 28 | 2020 |
| Implications of the further emergence and spread of the SARS CoV 2 B. 1.1. 529 variant of concern (Omicron) for the EU/EEA first update TA Brief European Centre for Disease Prevention and Control, 2021 | 27 | 2021 |
| Network-based prediction of COVID-19 epidemic spreading in Italy C Pizzuti, A Socievole, B Prasse, P Van Mieghem Applied Network Science 5, 1-22, 2020 | 24 | 2020 |
| Predicting network dynamics without requiring the knowledge of the interaction graph B Prasse, P Van Mieghem Proceedings of the National Academy of Sciences 119 (44), e2205517119, 2022 | 21* | 2022 |
| The viral state dynamics of the discrete-time NIMFA epidemic model B Prasse, P Van Mieghem IEEE Transactions on Network Science and Engineering 7 (3), 1667-1674, 2019 | 16 | 2019 |
| Predicting time‐resolved electrophysiological brain networks from structural eigenmodes P Tewarie, B Prasse, J Meier, K Mandke, S Warrington, CJ Stam, ... Human brain mapping 43 (14), 4475-4491, 2022 | 15 | 2022 |
| Clustering for epidemics on networks: A geometric approach B Prasse, K Devriendt, P Van Mieghem Chaos: an interdisciplinary journal of nonlinear science 31 (6), 2021 | 14 | 2021 |
| Public health considerations for transitioning beyond the acute phase of the COVID-19 pandemic in the EU/EEA JE Suk, A Pharris, J Beauté, E Colzani, H Needham, J Kinsman, R Niehus, ... Eurosurveillance 27 (17), 2200155, 2022 | 11 | 2022 |
| The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study J Wambua, N Loedy, CI Jarvis, KLM Wong, C Faes, R Grah, B Prasse, ... BMC Public Health 23 (1), 1350, 2023 | 10 | 2023 |
| Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models P Tewarie, B Prasse, J Meier, A Byrne, M De Domenico, CJ Stam, ... New Journal of Physics 23 (6), 063065, 2021 | 10 | 2021 |
| Exact network reconstruction from complete SIS nodal state infection information seems infeasible B Prasse, P Van Mieghem IEEE Transactions on Network Science and Engineering 6 (4), 748-759, 2018 | 10 | 2018 |
| Accuracy of predicting epidemic outbreaks B Prasse, MA Achterberg, P Van Mieghem Physical Review E 105 (1), 014302, 2022 | 9* | 2022 |
| Network reconstruction and prediction of epidemic outbreaks for NIMFA processes B Prasse, P Van Mieghem arXiv preprint arXiv:1811.06741, 2018 | 9 | 2018 |
| Maximum-likelihood network reconstruction for SIS processes is NP-hard B Prasse, P Van Mieghem arXiv preprint arXiv:1807.08630, 2018 | 8 | 2018 |
| Analysis of continuous-time Markovian ɛ-SIS epidemics on networks MA Achterberg, B Prasse, P Van Mieghem Physical Review E 105 (5), 054305, 2022 | 7 | 2022 |
Piet Van MieghemProfessor, Delft University of TechnologyVerified email at tudelft.nl
