Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England
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Abstract
The time-varying reproduction number ( R t : the average number of secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. While new infections are not usually observed directly, they can be estimated from data. However, data may be delayed and potentially biased. We investigated the sensitivity of R t estimates to different data sources representing COVID-19 in England, and we explored how this sensitivity could track epidemic dynamics in population sub-groups. We sourced public data on test-positive cases, hospital admissions and deaths with confirmed COVID-19 in seven regions of England over March through August 2020. We estimated R t using a model that mapped unobserved infections to each data source. We then compared differences in R t with the demographic and social context of surveillance data over time. Our estimates of transmission potential varied for each data source, with the relative inconsistency of estimates varying across regions and over time. R t estimates based on hospital admissions and deaths were more spatio-temporally synchronous than when compared to estimates from all test positives. We found these differences may be linked to biased representations of subpopulations in each data source. These included spatially clustered testing, and where outbreaks in hospitals, care homes, and young age groups reflected the link between age and severity of the disease. We highlight that policy makers could better target interventions by considering the source populations of R t estimates. Further work should clarify the best way to combine and interpret R t estimates from different data sources based on the desired use.
This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
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SciScore for 10.1101/2020.10.18.20214585: (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:This is a limitation of monitoring epidemic dynamics using test-positive surveillance data in areas where testing rates vary across the population and over time. This also suggests that Rt from admissions may be more reliable than that from all test-positive cases for indicating the relative intensity of an epidemic over time [36]. We hypothesised that variations in Rt estimates based on data reflecting more severe outcomes (hospital …
SciScore for 10.1101/2020.10.18.20214585: (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:This is a limitation of monitoring epidemic dynamics using test-positive surveillance data in areas where testing rates vary across the population and over time. This also suggests that Rt from admissions may be more reliable than that from all test-positive cases for indicating the relative intensity of an epidemic over time [36]. We hypothesised that variations in Rt estimates based on data reflecting more severe outcomes (hospital admissions and deaths) were related to changes in the age distribution of cases over time, because age is associated with severity [37]. We found that from June onwards, Rt from all test-positive cases appeared to increasingly diverge away from Rt from admissions and deaths, transitioning into a separate, higher, steady state. This was followed by the observed age distribution of all test-positive cases becoming increasingly younger, while the age distribution of admissions remained approximately level. Because of the severity gradient, this suggested the Rts from all test-positive cases and admissions were more biased by the relative proportion of younger cases and older cases respectively than the Rt from admissions or deaths. Similarly, all regions saw a near-synchronous local peak in Rt from hospital admissions over spring, which was not observed in Rt from test-positive cases. This may have reflected the known widespread regional outbreaks in care homes, with an older population who are more likely to experience fatal outcomes, and less like...
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.
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