Monitoring the evolving impacts of non-pharmaceutical intervention measures requires fine-grained estimation of transmission dynamics. We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction into mitigation and suppression factors for quantifying their impacts at a finer granularity.  Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in Europe and alert that 30 states in the United States are facing resurgence risks .
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 .
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Monitoring the evolving impacts of non-pharmaceutical intervention measures requires fine-grained estimation of transmission dynamics. We propose a framework to estimate instantaneous reproduction number R_t using Bayesian inference upon a renewal process, disentangling the R_t reduction
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
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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
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Europe and alert that 30 states in the United States are facing resurgence risks
these countries and the resurgence risk in the USA