On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate
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Abstract
The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and recoveries. These include time- and severity-dependent reporting of cases as well as time lags in observed patient outcomes. In the context of COVID-19 CFR estimation, we survey such biases and their potential significance. Further, we analyze theoretically the effect of certain biases, like preferential reporting of fatal cases, on naive estimators of CFR. We provide a partially corrected estimator of these naive estimates that accounts for time lag and imperfect reporting of deaths and recoveries. We show that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death. Our analysis is supplemented by theoretical and numerical results and a simple and fast open-source codebase . 1
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SciScore for 10.1101/2020.06.15.20038489: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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:Indeed, the relative CFR of Spain with respect to Korea (two countries whose time-dependent reporting rates are probably different) yields a value of 30.27, likely speaking to the limitations of this method, although we do not have ground-truth. Although Reich et al. (2012) present extensive experimental evaluations and some …
SciScore for 10.1101/2020.06.15.20038489: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. 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:Indeed, the relative CFR of Spain with respect to Korea (two countries whose time-dependent reporting rates are probably different) yields a value of 30.27, likely speaking to the limitations of this method, although we do not have ground-truth. Although Reich et al. (2012) present extensive experimental evaluations and some theory indicating that the method outperforms Enaive under given modeling assumptions, it is not generally possible to check how closely these assumptions hold, due to overparameterization of the unrestricted model. This issue may be mitigated by working with domain experts who understand each group’s sampling and reporting patterns. Another issue is that our estimator uses parameters ηt that are not estimated strictly from surveillance data but rather from individualized death times (Linton et al., 2020). We believe that the maximum-likelihood estimator that we have presented may provide a more valid correction of relative reporting rates between German women and men rather than between South Korean people and Italian people, given that reporting rates by sex may be closer to identical than reporting rates by country, although biases by sex still exist (Guerra-Silveira and Abad-Franch, 2013). Demographers have argued that releasing data stratified by sex, age, and other demographic groups would aid in understanding the spread and fatality rates of COVID-19 (Dowd et al., 2020). Although certain teams like Riffe (2020) are currently assembling this data, m...
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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