Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics
Research output: Contribution to journal › Journal article › Research › peer-review
Newly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
Original language | English |
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Article number | 1 |
Journal | Bulletin of Mathematical Biology |
Volume | 83 |
Issue number | 1 |
ISSN | 0092-8240 |
DOIs | |
Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:
We thank Klaus Dietz, Tübingen, for comments on the manuscript. This work was supported by a grant from Deutsche Forschungsgemeinschaft, Germany (SFB 1294, Project No. 318763901).
Publisher Copyright:
© 2020, The Author(s).
- COVID-19, Ensemble Kalman filter, Sequential data assimilation, Stochastic epidemic model
Research areas
ID: 389895183