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

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 a

SUBMITTER: Engbert R 

PROVIDER: S-EPMC7721793 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature