On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity
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Abstract
The pandemic spread of the COVID-19 virus has, as of 20 th of April 2020, reached most countries of the world. In an effort to design informed public health policies, many modelling studies have been performed to predict crucial outcomes of interest, including ICU solicitation, cumulated death counts, etc… The corresponding data analyses however, mostly rely on restricted (openly available) data sources, which typically include daily death rates and confirmed COVID cases time series. In addition, many of these predictions are derived before the peak of the outbreak has been observed yet (as is still currently the case for many countries). In this work, we show that peak phase and data paucity have a substantial impact on the reliability of model predictions. Although we focus on a recent model of the COVID pandemics, our conclusions most likely apply to most existing models, which are variants of the so-called “Susceptible-Infected-Removed” or SIR framework. Our results highlight the need for performing systematic reliability evaluations for all models that currently inform public health policies. They also motivate a plea for gathering and opening richer and more reliable data time series (e.g., ICU occupancy, negative test rates, social distancing commitment reports, etc).
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SciScore for 10.1101/2020.04.24.20078485: (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:All these limitations are difficult (though not impossible) to account for, and further challenge even further the reliability of model-based predictions. Contrary to most papers that focus on model definition and extension, the approach here tackles this assessment which we believe will become more and more important as more alternative models are proposed, to account for, e.g., the influence of lockdown decisions. This applies to the …
SciScore for 10.1101/2020.04.24.20078485: (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:All these limitations are difficult (though not impossible) to account for, and further challenge even further the reliability of model-based predictions. Contrary to most papers that focus on model definition and extension, the approach here tackles this assessment which we believe will become more and more important as more alternative models are proposed, to account for, e.g., the influence of lockdown decisions. This applies to the DCM-COVID model we evaluate here, which is currently being refined along these lines. The kind of data that may need to be acquired to inform the ensuing model predictions is an issue of primary practical importance if this or similar models are to guide public health decisions. Performing this type of analysis for currently available models is beyond the scope of the current work. However, our results highlight the need for evaluating the reliability of model predictions that are currently used by national and international socio-political decision makers. They also motivate the gathering of multiple data time series and making them available to the modelling community. This requirement obviously extends beyond ICU occupancy and negative test rates (Chen et al., 2020; Salomon, 2020). In the near future for instance, data about the number of asymptomatic cases in the population, about how infectious are children or about individual immunity after recovery may prove critical. In order to validate model predictions, particularly those related to ...
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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