Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics
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Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics. / Engbert, Ralf; Rabe, Maximilian M.; Kliegl, Reinhold; Reich, Sebastian.
In: Bulletin of Mathematical Biology, Vol. 83, No. 1, 1, 01.2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics
AU - Engbert, Ralf
AU - Rabe, Maximilian M.
AU - Kliegl, Reinhold
AU - Reich, Sebastian
N1 - 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).
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - Ensemble Kalman filter
KW - Sequential data assimilation
KW - Stochastic epidemic model
UR - http://www.scopus.com/inward/record.url?scp=85097293022&partnerID=8YFLogxK
U2 - 10.1007/s11538-020-00834-8
DO - 10.1007/s11538-020-00834-8
M3 - Journal article
C2 - 33289877
AN - SCOPUS:85097293022
VL - 83
JO - Bulletin of Mathematical Biology
JF - Bulletin of Mathematical Biology
SN - 0092-8240
IS - 1
M1 - 1
ER -
ID: 389895183