Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework
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Abstract
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, R t , 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 R t . We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of R t 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 R t 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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SciScore for 10.1101/2021.05.17.21256818: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Ascertainment bias model – assumptions and caveats: Debias-1 Spatial homogeneity of δ across LTLAs within a PHE region. The fact that we see relatively low variation in δ at each time point across PHE regions in Figure 3, particularly after October 2020, is consistent with a finer-scale spatial homogeneity assumption being reasonable. Debias-2 We handle prevalence in a reduced-dimension space of bins as described in SI section …
SciScore for 10.1101/2021.05.17.21256818: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Ascertainment bias model – assumptions and caveats: Debias-1 Spatial homogeneity of δ across LTLAs within a PHE region. The fact that we see relatively low variation in δ at each time point across PHE regions in Figure 3, particularly after October 2020, is consistent with a finer-scale spatial homogeneity assumption being reasonable. Debias-2 We handle prevalence in a reduced-dimension space of bins as described in SI section Interval-based prevalence inference – set-up and assumptions Debias-3 (In)stability of ascertainment mechanism. It is clear from Figure 3 that the ascertainment effects captured by δ can change rapidly and without obvious cause over time. Contemporaneous randomised surveillance data, such as REACT or ONS CIS, allow estimation of δ. However, when predicting prevalence forward in time beyond availability of randomised surveillance data, we are making the implicit assumption that the ascertainment bias remains stable forwards in time, and such results should therefore be interpreted with caution. PCR+ to infectious mapping – assumptions and caveats: For full details please see Supplementary Information—PCR positive to infectious mapping – method details. Infectious-1 Pillar 1+2 positive test counts, across a four-week period, are used as an approximation to the true relative incidence over that time interval at coarse-scale level (e.g. PHE region). Infectious-2 The probability (with credible intervals) of testing PCR positive when swabbed d days post infec...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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