Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework
| Autor: | George Nicholson, Brieuc Lehmann, Tullia Padellini, Koen B. Pouwels, Radka Jersakova, James Lomax, Ruairidh E. King, Ann-Marie Mallon, Peter J. Diggle, Sylvia Richardson, Marta Blangiardo, Chris Holmes |
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| Přispěvatelé: | Nicholson, George [0000-0001-9588-6075], Lehmann, Brieuc [0000-0002-7302-4391], Pouwels, Koen B [0000-0001-7097-8950], King, Ruairidh E [0000-0001-6733-8805], Richardson, Sylvia [0000-0003-1998-492X], Apollo - University of Cambridge Repository, Pouwels, Koen B. [0000-0001-7097-8950], King, Ruairidh E. [0000-0001-6733-8805] |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Microbiology (medical)
Epidemiology Immunology Basic Reproduction Number Applied Microbiology and Biotechnology Microbiology 03 medical and health sciences 0302 clinical medicine COVID-19 Testing Spatio-Temporal Analysis Bias 692/699/255/2514 1108 Medical Microbiology Genetics Prevalence Humans 030212 general & internal medicine 692/308/174 030304 developmental biology 0303 health sciences Models Statistical SARS-CoV-2 article COVID-19 Reproducibility of Results Cell Biology United Kingdom 3. Good health Viral infection Forecasting 0605 Microbiology |
| Zdroj: | Nature Microbiology |
| DOI: | 10.1038/s41564-021-01029-0 |
| Popis: | 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. A causal debiasing framework provides accurate estimates of local prevalence and effective reproduction number for surveillance of SARS-CoV-2 cases using data from randomized testing schemes to model ascertainment bias in targeted subpopulation data. |
| Databáze: | OpenAIRE |
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