@article{EngbertRabeKliegletal.2021,
  author    = {Engbert, Ralf and Rabe, Maximilian Michael and Kliegl, Reinhold and Reich, Sebastian},
  title     = {Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics},
  series = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology},
  volume    = {83},
  journal   = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology},
  number    = {1},
  publisher = {Springer},
  address   = {New York},
  issn      = {0092-8240},
  doi       = {10.1007/s11538-020-00834-8},
  pages     = {16},
  year      = {2021},
  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.},
  language  = {en}
}