论文标题
固定时间的描述性统计数据低估了流行曲线的极端统计
Fixed-time descriptive statistics underestimate extremes of epidemic curve ensembles
论文作者
论文摘要
在世界范围内，学者们正在竞赛，以预测新颖的冠状病毒，Covid-19。这种预测通常是通过数值模拟相关参数合理组合的流行病来实现的。至关重要的是，从所得的模拟曲线集合得出的流行轨迹的任何预测都以置信区间表示，以传达与预测相关的不确定性。在这里，我们认为总结集成统计的最新方法不会捕获关键的流行病学信息。特别是，当前的方法系统地抑制了有关投影轨迹峰的信息。基本问题是，在统计分析中，每个时间步骤都会分别处理。我们建议使用基于曲线的描述性统计来汇总轨迹集合。结果提出的结果使研究人员能够报告更多代表性的置信区间，从而导致对流行轨迹的更现实的预测，并且 - 反过来又在面对当前和未来的大流行时可以更好地决策。
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.
