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 -