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Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics.
- Source :
-
Bulletin of mathematical biology [Bull Math Biol] 2020 Dec 08; Vol. 83 (1), pp. 1. Date of Electronic Publication: 2020 Dec 08. - Publication Year :
- 2020
-
Abstract
- 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.
- Subjects :
- Asymptomatic Infections epidemiology
Basic Reproduction Number statistics & numerical data
COVID-19 transmission
Computer Simulation
Data Interpretation, Statistical
Germany epidemiology
Humans
Likelihood Functions
Mathematical Concepts
Models, Biological
Models, Statistical
Stochastic Processes
Time Factors
COVID-19 epidemiology
Pandemics statistics & numerical data
SARS-CoV-2
Subjects
Details
- Language :
- English
- ISSN :
- 1522-9602
- Volume :
- 83
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Bulletin of mathematical biology
- Publication Type :
- Academic Journal
- Accession number :
- 33289877
- Full Text :
- https://doi.org/10.1007/s11538-020-00834-8