The statistical challenges of modelling COVID-19

Publication date: 1 Oct 2021 | Publication type: National Institute Economic Review | NIESR Author(s): Dolton, P | JEL Classification: C54; I18; C32; C33 | Journal: National Institute Economic Review Vol. 257 | Publisher: Cambridge University Press

In 2020–2021, the world has been gripped by a pandemic that no living person has ever known. The coronavirus pandemic is undoubtedly the greatest challenge the world has faced in over a generation. The imperative of statistical modelling is not only to manage the short-run crisis for the health services, but also to explain the pandemic’s course and establish the effectiveness of different policies, both non-pharmaceutical and with vaccines. This difficult task has been undertaken by the epidemiologists and others in the face of measurement data problems, behavioural complications and endogeneity issues. This paper proposes a simple taxonomy of the alternative different models and suggests how they may be used together to overcome limitations. This perspective may have important implications for how policy-makers cope with future waves or strains in the current pandemic, or future pandemics.

Keyword tags: 
COVID-19
econometric modelling