COVID-19 and the difficulty of inferring epidemiological parameters from clinical data

Simon N Wood*, Ernst C Wit, Matteo Fasiolo, Peter J Green

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
12 Downloads (Pure)

Abstract

Knowing the infection fatality ratio (IFR) is of crucial importance for evidence-based epidemic management: for balancing the life years saved against the life years lost due the consequences of such management and for evaluating the ethical issues associated with the willingness to pay only for life years lost to the epidemic, but not to other diseases. Against this background, in an impressive paper, Verity et al. (2020) have rapidly assembled case data and used statistical modelling to infer the IFR for COVID-19.

Given the importance of the issues, the necessarily compromised nature of the data and the consequent heavy reliance on modelling assumptions, we believe that an in-depth statistical review of what has been done is useful. We have attempted this, conscious that the circumstances require setting aside the usual standards of statistical nit-picking. Facilitated by Verity et al. (2020)'s exemplary provision of their code and data, we have attempted to identify the extent to which the data may be sufficiently informative of the IFR that it plays a greater role than the modelling assumptions, and have tried to identify those assumptions that appear to play a key role.
Original languageEnglish
Pages (from-to)27-28
Number of pages2
JournalLancet Infectious Diseases
Volume21
Issue number1
Early online date28-May-2020
DOIs
Publication statusPublished - Jan-2021
Externally publishedYes

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