EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation.
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ABSTRACT:
SUBMITTER: Biegel HR
PROVIDER: S-EPMC8132228 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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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Project description:[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].
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