Bayesian estimation of real-time epidemic growth rates using Gaussian processes: local dynamics of SARS-CoV-2 in England
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Abstract
Quantitative assessments of the recent state of an epidemic and short-term projections for the near future are key public-health tools that have substantial policy impacts, helping to determine if existing control measures are sufficient or need to be strengthened. Key to these quantitative assessments is the ability to rapidly and robustly measure the speed with which an epidemic is growing or decaying. Frequently, epidemiological trends are addressed in terms of the (time-varying) reproductive number R. Here, we take a more parsimonious approach and calculate the exponential growth rate, r, using a Bayesian hierarchical model to fit a Gaussian process to the epidemiological data. We show how the method can be employed when only case data from positive tests are available, and the improvement gained by including the total number of tests as a measure of the heterogeneous testing effort. Although the methods are generic, we apply them to SARS-CoV-2 cases and testing in England, making use of the available high-resolution spatio-temporal data to determine long-term patterns of national growth, highlight regional growth, and spatial heterogeneity.
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SciScore for 10.1101/2022.01.01.21268131: (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.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our approach for estimating the growth rate is a purely statistical method and therefore has limitations. First, the model is non-mechanistic and does not incorporate any epidemiological assumptions. Therefore, it is not suitable for predicting future changes in infections or making long term forecasts, particularly as it cannot account for the depletion of susceptible through infection or vaccination. Second, we assume that the spatial regions …
SciScore for 10.1101/2022.01.01.21268131: (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.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our approach for estimating the growth rate is a purely statistical method and therefore has limitations. First, the model is non-mechanistic and does not incorporate any epidemiological assumptions. Therefore, it is not suitable for predicting future changes in infections or making long term forecasts, particularly as it cannot account for the depletion of susceptible through infection or vaccination. Second, we assume that the spatial regions investigated are independent and homogeneous, we do not account for the movement of infection between regions (Kraemer et al., 2021) nor the spatial and social structure within a region. A lack of internal structure could be important for public-health concerns; for example, an outbreak that is primarily increasing in the young has very different health implications compared to one that is increasing in the elderly. There is no reason why richer data structures cannot be incorporated within our methodology (for example looking at the growth rate in a set of age-groups), but such an analysis requires large amounts of data and is increasing complex to interpret. Third, the data analysed in this study comes from PCR testing (or individuals that have performed a lateral flow test followed by PCR). Therefore, there are limitations due to specificity and sensitivity of the test and the ability of individuals to swab reliably. Associated with this, and discussed above, changes to test-seeking behaviour beyond a simple increase in testing coul...
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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