Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles

TL;DR

This paper highlights that current methods for summarizing epidemic curve ensembles underestimate the peaks of outbreaks, proposing curve-based statistics for more accurate confidence intervals and better epidemic forecasting.

Contribution

It introduces a novel approach using curve-based descriptive statistics to improve the accuracy of epidemic trajectory summaries compared to traditional pointwise methods.

Findings

Current methods suppress peak information in epidemic forecasts.

Curve-based statistics provide more realistic confidence intervals.

Improved summaries aid better decision making during pandemics.

Abstract

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.