Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229513
Authors: 
Pesaran, M. Hashem
Yang, Cynthia Fan
Year of Publication: 
2020
Series/Report no.: 
CESifo Working Paper No. 8695
Abstract: 
This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate identification and estimation of recovery and transmission rates. The paper then proposes simple moment-based rolling estimates and shows them to be fairly robust to the well-known under-reporting of infected cases. Empirical evidence on six European countries match the simulated outcomes, once the under-reporting of infected cases is addressed. It is estimated that the number of reported cases could be between 3 to 9 times lower than the actual numbers. Counterfactual analysis using calibrated models for Germany and UK show that early intervention in managing the infection is critical in bringing down the reproduction numbers below unity in a timely manner.
Subjects: 
Covid-19
multigroup SIR model
basic and effective reproduction numbers
rolling window estimates of the transmission rate
method of moments
calibration and counterfactual analysis.
JEL: 
C13
C15
C31
D85
I18
J18
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.