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Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles

by   Jonas L. Juul, et al.

Across the world, scholars are racing to predict the spread of the novel coronavirus, COVID-19. Such predictions are often pursued by numerically simulating epidemics with a large number of plausible combinations of relevant parameters. It is essential that any forecast of the epidemic trajectory derived from the resulting ensemble of simulated curves is presented with confidence intervals that communicate the uncertainty associated with the forecast. Here we argue that the state-of-the-art approach for summarizing ensemble statistics does not capture crucial epidemiological information. In particular, the current approach systematically suppresses information about the projected trajectory peaks. The fundamental problem is that each time step is treated separately in the statistical analysis. We suggest using curve-based descriptive statistics to summarize trajectory ensembles. The results presented allow researchers to report more representative confidence intervals, resulting in more realistic projections of epidemic trajectories and – in turn – enable better decision making in the face of the current and future pandemics.


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The authors are thankful to the members of the SSI COVID-19 modeling group for an excellent collaboration and to Carl 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).

Author contributions

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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