NCEO data assimilation scientists contributed to a broad international effort to assess Covid-19 pandemic across seven countries.
This work shows how one can use iterative ensemble smoothers to effectively estimate parameters of a SEIR model with age-classes and compartments of sick, hospitalised, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalised. Also, it is possible to condition on the number of cases obtained from testing.
The updated ensemble of model simulations have predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development given knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries with vastly different developments of the epidemic, and we can accurately model the development of the COVID-19 outbreak in these countries.
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