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This paper develops an individual-based stochastic network SIR model for theempirical analysis of the Covid-19 pandemic. It derives moment conditions forthe number of infected and active cases for single as well as multigroupepidemic models. These moment conditions are used to investigate theidentification and estimation of the transmission rates. The paper thenproposes a method that jointly estimates the transmission rate and themagnitude of under-reporting of infected cases. Empirical evidence on sixEuropean countries matches the simulated outcomes once the under-reporting ofinfected cases is addressed. It is estimated that the number of actual casescould be between 4 to 10 times higher than the reported numbers in October 2020and declined to 2 to 3 times in April 2021. The calibrated models are used inthe counterfactual analyses of the impact of social distancing and vaccinationon the epidemic evolution, and the timing of early interventions in the UK andGermany.

Matching Theory and Evidence on Covid-19 using a Stochastic Network SIR Model