Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model
M Pesaran () and
Cynthia Fan Yang ()
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
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.
Keywords: Covid-19; multigroup SIR model; basic and effective reproduction numbers; rolling window estimates of the transmission rate; method of moments; calibration and counterfactual analysis (search for similar items in EconPapers)
JEL-codes: C13 C15 C31 D85 I18 J18 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
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Working Paper: Matching Theory and Evidence on Covid-19 Using a Stochastic Network SIR Model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:20102
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