Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic
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Abstract
Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.
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SciScore for 10.1101/2020.12.08.20238154: (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:We note a few limitations of our approach that are inherited from the EpiEstim estimator. First, the maximum length of serial interval s and the sliding window size τ are subjective choices [21]. Second, it is possible to have negative serial intervals for COVID-19 which is currently not allowed by EpiEstim [25]. Third, it is most accurate to estimate reproductive numbers from the incidence curve rather than the epidemic curve for …
SciScore for 10.1101/2020.12.08.20238154: (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:We note a few limitations of our approach that are inherited from the EpiEstim estimator. First, the maximum length of serial interval s and the sliding window size τ are subjective choices [21]. Second, it is possible to have negative serial intervals for COVID-19 which is currently not allowed by EpiEstim [25]. Third, it is most accurate to estimate reproductive numbers from the incidence curve rather than the epidemic curve for EpiEstim [21, 26]. However, infection events would be very hard, if not impossible, to observe for the current pandemic and thus strong parametric assumptions are likely needed [12, 14], which is beyond the scope of this paper. Fourth, reproductive number estimates will be less trustworthy if the fraction of infection observed is not constant over time [3, 20, 27]. For COVID-19, this is likely the case considering the evolution of testing and the significant proportion of asymptomatic transmission [28], requiring further adjustment of the data. Empirically, there are some important issues to consider in properly implementing our method. First, our model is region-specific, i.e., one need to fit our model to line list data of a single region to avoid systematic differences between regions. The region is defined such that each region is deemed to have its own reporting system (and thus its unique reporting delay distribution). For example, if the reporting system differs at the county level, we should use line list data of each county (rather than eac...
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.
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