Author: Erida Gjini
Title: Modeling Covid-19 dynamics for real-time estimates and projections: an application to Albanian data Document date: 2020_3_23
ID: ela022bo_41
Snippet: This is a simple model embedded in a Bayesian framework, to fit real-time data of early COVID-19 dynamics, estimate parameters and generate predictions, accounting for intrinsic uncertainty. The model is generic and can be applied to any setting. I show that it captures well the data from March 9 to March 28 in Albania, and suggests that the effects of control are already tangible in the dynamics. A dynamic control put in place almost immediately.....
Document: This is a simple model embedded in a Bayesian framework, to fit real-time data of early COVID-19 dynamics, estimate parameters and generate predictions, accounting for intrinsic uncertainty. The model is generic and can be applied to any setting. I show that it captures well the data from March 9 to March 28 in Albania, and suggests that the effects of control are already tangible in the dynamics. A dynamic control put in place almost immediately after the detection of the first case, has had an effect of reducing the original transmission, already by 30% in the first week of implementation. The projections are based on maintaining control from the onset of intervention, T in , throughout the period simulated.
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