Early warning signal reliability varies with COVID-19 waves

<strong>Abstract</strong> Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation, and skewness can predict non-linear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of urgent intervention decisions but best characterise hypothesised critical transitions. <strong>Dataset</strong> The deposited dataset contains scripts used in the early warning signal and generalised additive model analysis, the generation of figures, and the custom R functions underpinning the work. Raw COVID-19 case data is also provided if users prefer to access files directly rather than sourcing from the host repositories (all credit is provided to the original publishers).<strong>摘要</strong> 早期预警信号（Early Warning Signals, EWSs）旨在通过时间序列数据中的现象学信号，预测复杂系统的动态变化。近期研究表明，此类信号可在疾病暴发前出现，为决策者制定前瞻性而非被动响应的管理决策带来了可能。本研究结合24个国家的每日新冠确诊病例数据，采用新颖的序列分析方法，提出由方差、自相关与偏度构成的复合早期预警信号能够预测确诊病例的非线性增长；但此类工具的预测能力会随不同疫情波次存在差异，具体取决于系统当前的临界减缓程度。本研究表明，在新冠疫情这类监测完备的疾病时间序列中，早期预警信号可为决策者提升紧急干预决策的准确性提供助力，同时也能更好地表征所假设的临界转变过程。<strong>数据集</strong> 本提交数据集包含本研究中用于早期预警信号与广义加性模型分析、图表生成的脚本，以及支撑本研究的自定义R函数。若用户希望直接获取原始数据而非从宿主仓库调取，本数据集也提供了原始新冠确诊病例数据（所有荣誉归于原始发布者）。