Author: Ralf Engbert; Maximilian M. Rabe; Reinhold Kliegl; Sebastian Reich
Title: Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics Document date: 2020_4_17
ID: 855am0mv_5
Snippet: The key motivation of the current study was to apply sequential data assimilation of the stochastic SEIR model to estimate the contact parameter. Using simulated data, we successfully applied an ensemble Kalman filter [11, 20] for recovery of the contact parameter from data (see Parameter recovery from simulated data). When applied to empirical data on the level of a region, the estimation of the contact parameter produces a comparable evidence p.....
Document: The key motivation of the current study was to apply sequential data assimilation of the stochastic SEIR model to estimate the contact parameter. Using simulated data, we successfully applied an ensemble Kalman filter [11, 20] for recovery of the contact parameter from data (see Parameter recovery from simulated data). When applied to empirical data on the level of a region, the estimation of the contact parameter produces a comparable evidence profile (see Application to empirical data).
Search related documents:
Co phrase search for related documents- ensemble Kalman filter and SEIR model: 1, 2
- ensemble Kalman filter and stochastic SEIR model: 1, 2
- Kalman filter and SEIR model: 1, 2, 3, 4, 5, 6
- Kalman filter and stochastic SEIR model: 1, 2, 3
- Parameter recovery and SEIR model: 1, 2
- Parameter recovery and stochastic SEIR model: 1
- region level and SEIR model: 1, 2
- SEIR model and stochastic SEIR model: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74
Co phrase search for related documents, hyperlinks ordered by date