EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation.
Ontology highlight
ABSTRACT:
SUBMITTER: Biegel HR
PROVIDER: S-EPMC8132228 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
Publications
EpiCovDA: a mechanistic COVID-19 forecasting model with data assimilation.
Biegel Hannah R HR Lega Joceline J
ArXiv 20210512
We introduce a minimalist outbreak forecasting model that combines data-driven parameter estimation with variational data assimilation. By focusing on the fundamental components of nonlinear disease transmission and representing data in a domain where model stochasticity simplifies into a process with independent increments, we design an approach that only requires four core parameters to be estimated. We illustrate this novel methodology on COVID-19 forecasts. Results include case count and dea ...[more]
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