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Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles

The uncertainty associated with epidemic forecasts is often simulated with ensembles of epidemic trajectories based on combinations of parameters. We show that the standard approach for summarizing such ensembles systematically suppresses critical epidemiological information.

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Fig. 1: Pitfalls in using fixed-time descriptive statistics to summarize ensembles of epidemic curves.
Fig. 2: Curve-based descriptive statistics to summarize curve ensembles.

Code availability

The code to reproduce all figures is available at www.github.com/jonassjuul/curvestat. A Python package, ‘curvestat’, to produce the curve-based descriptive statistics used in this Comment can be cloned from www.github.com/jonassjuul/curvestat. Ensembles in Fig. 2 were produced using a deterministic compartmental model with sampling of parameters based on literature and expert opinions, which is described in detail at https://files.ssi.dk/teknisk-gennemgang-af-modellerne-10062020. The R code used to produce the ensemble is available at https://github.com/laecdtu/C19DK.

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Acknowledgements

We thank the members of the SSI COVID-19 modelling group for an excellent collaboration and C. T. Bergstrom for comments on an early version of the manuscript. J.L.J. and S.L. received additional funding through the HOPE project (Carlsberg Foundation).

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J.L.J. and S.L. conceived the idea. J.L.J. performed simulations, analysis and calculations. K.G. and L.E.C. devised and performed epidemiological simulations. All authors contributed to discussions and wrote the manuscript.

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Correspondence to Jonas L. Juul or Sune Lehmann.

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Juul, J.L., Græsbøll, K., Christiansen, L.E. et al. Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles. Nat. Phys. 17, 5–8 (2021). https://doi.org/10.1038/s41567-020-01121-y

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