Journal article · Preprint article
Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
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.
Language: | English |
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Publisher: | Nature Publishing Group UK |
Year: | 2021 |
Pages: | 5-8 |
ISSN: | 17452481 and 17452473 |
Types: | Journal article and Preprint article |
DOI: | 10.1038/s41567-020-01121-y |
ORCIDs: | 0000-0002-5728-9269 , Græsbøll, Kaare , Jørgensen, Sune Lehmann and Christiansen, Lasse Engbo |