Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
- 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 areNewly 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.…
Author details: | Ralf EngbertORCiDGND, Maximilian Michael RabeORCiDGND, Reinhold KlieglORCiDGND, Sebastian ReichORCiDGND |
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DOI: | https://doi.org/10.1007/s11538-020-00834-8 |
ISSN: | 0092-8240 |
ISSN: | 1522-9602 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33289877 |
Title of parent work (English): | Bulletin of mathematical biology : official journal of the Society for Mathematical Biology |
Publisher: | Springer |
Place of publishing: | New York |
Publication type: | Article |
Language: | English |
Date of first publication: | 2020/12/08 |
Publication year: | 2021 |
Release date: | 2023/01/06 |
Tag: | COVID-19; Ensemble Kalman; Sequential data assimilation; Stochastic epidemic model; filter |
Volume: | 83 |
Issue: | 1 |
Article number: | 1 |
Number of pages: | 16 |
Funding institution: | Projekt DEAL |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie | |
DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie | |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |