SimInf: an R package for data-driven stochastic disease spread simulations S Widgren, P Bauer, R Eriksson, S Engblom arXiv preprint arXiv:1605.01421, 2016 | 72 | 2016 |
Bayesian epidemiological modeling over high-resolution network data S Engblom, R Eriksson, S Widgren Epidemics 32, 100399, 2020 | 17 | 2020 |
Robust and integrative Bayesian neural networks for likelihood-free parameter inference F Wrede, R Eriksson, R Jiang, L Petzold, S Engblom, A Hellander, ... 2022 International Joint Conference on Neural Networks (IJCNN), 1-10, 2022 | 8 | 2022 |
Bayesian monitoring of COVID-19 in Sweden R Marin, H Runvik, A Medvedev, S Engblom Epidemics 45, 100715, 2023 | 4 | 2023 |
Initialization of a disease transmission model H Runvik, A Medvedev, R Eriksson, S Engblom Ifac-papersonline 53 (5), 839-844, 2020 | 2 | 2020 |
Bayesian inference in epidemics: linear noise analysis S Bronstein, S Engblom, R Marin arXiv preprint arXiv:2203.10906, 2022 | | 2022 |
Bayesian Monitoring of COVID-19 in Sweden (preprint) R Marin, H Runvik, A Medvedev, S Engblom | | 2022 |
Computational Modeling, Parameterization, and Evaluation of the Spread of Diseases R Marin Acta Universitatis Upsaliensis, 2022 | | 2022 |
Towards Confident Bayesian Parameter Estimation in Stochastic Chemical Kinetics S Engblom, R Eriksson, P Vilanova Numerical Mathematics and Advanced Applications ENUMATH 2019: European …, 2020 | | 2020 |
Stochastic modeling and bayesian inference of national scale epidemics in the swedish cattle network R Eriksson, S Engblom Proceedings of the 2018 Winter Simulation Conference, 4190-4191, 2018 | | 2018 |
Towards Bayesian parametrization of national scale epidemics R Eriksson, S Engblom, S Widgren MATHMOD 2018, February 21–23, Vienna, Austria, 65-66, 2018 | | 2018 |