论文标题
使用光谱分析快速评估锁定策略：新每日Covid-19案件背后的周期以及锁定后发生的情况
Rapidly evaluating lockdown strategies using spectral analysis: the cycles behind new daily COVID-19 cases and what happens after lockdown
论文作者
论文摘要
光谱分析表征了振荡时间序列序列行为，例如周期，但是准确的估计需要合理的观测值。许多国家的当前COVID-19时间序列很短：前后的锁定后系列序列较短。对这种系列中潜在有趣的周期的准确估计似乎无法实现。我们通过使用最近的贝叶斯光谱融合方法来解决从短时间序列获得准确估计的问题。在这里，我们表明，许多国家的新每日共同19例案件通常包含在2.7、4.1和6.7天（每周）的波长下运行的三个周期。我们表明，锁定后较短的周期会被抑制。锁定前后的差异表明，每周的作用至少部分是由于非流行因素引起的，而两个较短的循环似乎是流行病的固有。不受约束的新病例呈指数增长，但内部循环结构导致周期性下降。这表明，锁定成功只能在案件中每天四个或更多的每日跌倒来指示。流行时间序列的光谱学习有助于理解流行过程，有助于评估干预措施并有助于预测。光谱融合是一种通用技术，能够以不同的采样率记录的光谱融合，可以将其应用于许多学科的广泛时间序列。
Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. Current COVID-19 time series for many countries are short: pre- and post-lockdown series are shorter still. Accurate estimation of potentially interesting cycles within such series seems beyond reach. We solve the problem of obtaining accurate estimates from short time series by using recent Bayesian spectral fusion methods. Here we show that transformed new daily COVID-19 cases for many countries generally contain three cycles operating at wavelengths of around 2.7, 4.1 and 6.7 days (weekly). We show that the shorter cycles are suppressed after lockdown. The pre- and post lockdown differences suggest that the weekly effect is at least partly due to non-epidemic factors, whereas the two shorter cycles seem intrinsic to the epidemic. Unconstrained, new cases grow exponentially, but the internal cyclic structure causes periodic falls in cases. This suggests that lockdown success might only be indicated by four or more daily falls in cases. Spectral learning for epidemic time series contributes to the understanding of the epidemic process, helping evaluate interventions and assists with forecasting. Spectral fusion is a general technique that is able to fuse spectra recorded at different sampling rates, which can be applied to a wide range of time series from many disciplines.
