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

Bull Math Biol. 2020 Dec 8;83(1):1. doi: 10.1007/s11538-020-00834-8.


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.

Keywords: COVID-19; Ensemble Kalman filter; Sequential data assimilation; Stochastic epidemic model.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asymptomatic Infections / epidemiology
  • Basic Reproduction Number / statistics & numerical data
  • COVID-19 / epidemiology*
  • COVID-19 / transmission
  • Computer Simulation
  • Data Interpretation, Statistical
  • Germany / epidemiology
  • Humans
  • Likelihood Functions
  • Mathematical Concepts
  • Models, Biological
  • Models, Statistical
  • Pandemics* / statistics & numerical data
  • SARS-CoV-2*
  • Stochastic Processes
  • Time Factors