  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.

Physics and Society;Populations and Evolution;Applications