Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Nicholson, George and Lehmann, Brieuc and Padellini, Tullia and Pouwels, Koen B and Jersakova, Radka and Lomax, James and King, Ruairidh E and Mallon, Ann-Marie and Diggle, Peter J and Richardson, Sylvia and Blangiardo, Marta and Holmes, Chris (2021) Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework. Nature Microbiology, 7 (1). pp. 97-107. ISSN 2058-5276

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Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

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Journal Article
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Nature Microbiology
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03 Mar 2022 15:15
Last Modified:
17 Sep 2023 03:12