Kevin Fong, Tim Cook, and Charlotte Summers’ contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al.

This paper discusses some statistical aspects of the UK Covid-19 pandemic response, focussing particularly on cases where we believe that a statistically questionable approach or presentation has had a substantial impact on public perception, or government policy, or both. We discuss the presentation of statistics relating to Covid risk, and the risk of the response measures, arguing that biases tended to operate in opposite directions, overplaying Covid risk and underplaying the response risks. We also discuss some issues around the presentation of life loss data, excess deaths, and the use of case data. The consequences of neglect of most individual variability from epidemic models, alongside the consequences of some other statistically important omissions, are also covered. Finally, the evidence that full stay-at-home lockdowns were necessary to reverse waves of infection is examined, with new analyses provided for several European countries.

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Publication Details

Journal
Journal of the Royal Statistical Society Series A (Statistics in Society)
Published
2025-07-19
DOI
https://doi.org/10.1093/jrsssa/qnaf109
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Kevin Fong, Tim Cook, and Charlotte Summers’ contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al.

Charlotte Summers, Tim Cook, Kevin Fong
Journal of the Royal Statistical Society Series A (Statistics in Society)
COVID-19 epidemiological studies
article

Kevin Fong, Tim Cook, and Charlotte Summers’ contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al.

Charlotte Summers, Tim Cook, Kevin Fong
article en

Abstract

This paper discusses some statistical aspects of the UK Covid-19 pandemic response, focussing particularly on cases where we believe that a statistically questionable approach or presentation has had a substantial impact on public perception, or government policy, or both. We discuss the presentation of statistics relating to Covid risk, and the risk of the response measures, arguing that biases tended to operate in opposite directions, overplaying Covid risk and underplaying the response risks. We also discuss some issues around the presentation of life loss data, excess deaths, and the use of case data. The consequences of neglect of most individual variability from epidemic models, alongside the consequences of some other statistically important omissions, are also covered. Finally, the evidence that full stay-at-home lockdowns were necessary to reverse waves of infection is examined, with new analyses provided for several European countries.

Journal of the Royal Statistical Society Series A (Statistics in Society)Vol. 189(1)
University College London Hospitals NHS Foundation Trust (GB), University of Cambridge (GB), Victor (Japan) (JP), University of Bristol (GB), Department of Science and Technology (IN), Royal United Hospital Bath NHS Trust (GB), University College London (GB)
Openalex Percentile: Top 79%
COVID-19 epidemiological studies
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Kevin Fong, Tim Cook, and Charlotte Summers’ contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al. — Charlotte Summers, Tim Cook, et al. · Journal of the Royal Statistical Society Series A (Statistics in Society) (2025) | TGRS Research Map | TGRS