  Epidemic models play a key role in understanding and responding to the
emerging COVID-19 pandemic. Widely used compartmental models are static and are
of limited use to evaluate intervention strategies with the emerging pandemic.
Applying the technology of data assimilation, we propose a Bayesian updating
approach for estimating epidemiological parameters using observable information
for the purpose of assessing the impacts of different intervention strategies.
We adopt a concise renewal model and propose new parameters by disentangling
the reduction of instantaneous reproduction number Rt into mitigation and
suppression factors for quantifying intervention impacts at a finer
granularity. Then we developed a data assimilation framework for estimating
these parameters including constructing an observation function and developing
a Bayesian updating scheme. A statistical analysis framework is then built to
quantify the impact of intervention strategies by monitoring the evolution of
these estimated parameters. By Investigating the impacts of intervention
measures of European countries, the United States and Wuhan with the framework,
we reveal the effects of interventions in these countries and the resurgence
risk in the USA.

Populations and Evolution;Physics and Society