Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics

被引:63
|
作者
Engbert, Ralf [1 ]
Rabe, Maximilian M. [1 ]
Kliegl, Reinhold [2 ]
Reich, Sebastian [3 ]
机构
[1] Univ Potsdam, Dept Psychol, Potsdam, Germany
[2] Univ Potsdam, Div Training & Movement Sci, Potsdam, Germany
[3] Univ Potsdam, Inst Math, Potsdam, Germany
关键词
Stochastic epidemic model; Sequential data assimilation; Ensemble Kalman filter; COVID-19;
D O I
10.1007/s11538-020-00834-8
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:16
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